papers_transfer_type.csv

ID\Name\URL\TransferExperimentType\TransferExperimentSubType\TransferDataType\TransferPerformanceMetrics\SuccessWithmetric\RL Implementation\RL Policy Type\Inter-Task Mappings\Autonomous Transfer?\Is Deep RL?\Special Novelty?\Application Field\Allowed Learners\Paper available?\Behind Paywall?\PDF available somewhere without paywall?\Paper Origin Country\Uni Name\Uni department\Paper for a Thesis? (0 = No, 1 = Is Thesis, 2 = Master, else ID)\Source Task Selection\Was in Survey (0 = No, 1 = yes and also here, 2 = yes but not in dataset)\Has transfer in Title\Has Transfer in Abstract\Has Transfer in Text\Additional Tags\Transfer-From-To\1 Rejected, 2 Unpublished\1 = part of phd, 2 = same but different length, 3 = duplicate, 4 = prepublish before conference acceptance, 5 = partially reused results, 6 = only some text overlap but new results, 7 = updated version, 8 = extended with partial figure reuse\2D Nav Level
2962858248\Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning\Some("https://ui.adsabs.harvard.edu/abs/2015arXiv151106342P/abstract")\["p", "r", "s", "#", "a"]\["lit"]\["weights"]\["j", "tt"]\\["Custom", "CS", "Theorem", "Lemma", "Formulas", "Figures", "Tables"]\["DQN", "AMN"]\["N/A"]\1\1\\["Simulation", "Games", "VideoGames", "Atari", "Generalization"]\["TD"]\1\0\0\["Canada"]\["University of Toronto"]\["Computer Science"]\0\["all"]\["mag", "zhu", "multi"]\["t", "r", "l", "ta"]\["t", "r", "l", "k", "de", "do", "g", "ta"]\["t", "r", "l", "k", "de", "do", "ta"]\\[["Atari", "Atari"]]\\\
2440926996\Successor Features for Transfer in Reinforcement Learning\Some("https://export.arxiv.org/pdf/1606.05312")\["s", "s_i", "s_f", "t"]\["multi"]\["fea", "pi"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Theorem", "Formulas", "Figures", "Tables", "Lemma"]\["DQN", "SFDQN", "SFQL", "PRQL"]\["N/A"]\0\1\\["2D", "Navigation", "Collect", "Simulation", "Robotics", "Mujoco", "Arm"]\["TD", "H"]\1\0\0\["USA"]\["DeepMind"]\["Industry"]\0\["all"]\["mag", "zhu"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2605368761\Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning\Some("https://openreview.net/pdf?id=Hyq4yhile")\["#", "t", "v", "a"]\["diff-no"]\["fea"]\["j", "tt", "ap"]\\["Custom", "CS", "Mujoco", "Figures", "Formulas", "Tables", "Videos"]\["PG"]\["exp"]\0\1\\["Robotics", "Simulation", "Mujoco", "Button", "Pull", "Push", "MultiAgent"]\["TD"]\1\0\0\["USA"]\["University of California", "OpenAI"]\["Electrical Engineering and Computer Science", "Industry", "Uni", "Colab"]\0\["all"]\["mag", "silva", "zhu"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\\\\\
2962985403\Universal Successor Representations for Transfer Reinforcement Learning\Some("https://openreview.net/pdf?id=HJ_CpYyDz")\["s_i", "s_f", "levels"]\["multi"]\["r", "fea"]\["ap", "tr", "tt", "j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["USRA", "AC"]\["exp"]\0\1\\["2D", "Navigation", "Grid", "Simulation", "Successor"]\["TD"]\1\0\0\["USA", "Canada"]\["University of Alberta", "University of Montreal"]\["Computer Science"]\0\["all"]\["mag", "zhu"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2963611966\DARLA: improving zero-shot transfer in reinforcement learning\Some("https://www.arxiv-vanity.com/papers/1707.08475/")\["t", "s_i", "s_f", "levels"]\["diff-no", "sim2real"]\["fea", "curriculum"]\["ap", "tr", "tt", "j"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Videos", "TensorFlow", "Mujoco", "DeepMind Lab"]\["DQN", "A3C", "EC", "UNREAL"]\["exp"]\1\1\\["Simulation", "Representation", "JacoArm", "DeepMind Lab", "Robotics"]\["TD"]\1\0\0\["UK"]\["DeepMind"]\["Industry"]\0\["all"]\["mag", "zhu"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
158722652\Transfer in Reinforcement Learning: a Framework and a Survey\Some("https://doi.org/10.1007/978-3-642-27645-3_5")\[]\["theory", "survey"]\[]\[]\\[]\[]\[]\0\0\\["theory", "survey"]\[]\1\0\0\["France"]\["University of Lille"]\["INRIA Lille"]\0\[]\["mag", "silva", "cur"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
1492014007\Building portable options: skill transfer in reinforcement learning\Some("http://ijcai.org/Proceedings/07/Papers/144.pdf")\["p"]\["diff-no"]\["options"]\["j", "tr"]\\["Custom", "CS", "Figures", "Formulas"]\["SARSA"]\["N/A"]\0\0\\["2D", "Navigation", "Collect", "Simulation"]\["TD", "H"]\1\0\0\["USA"]\["University of Massachusetts"]\["Computer Science"]\0\["h"]\["taylor", "mag", "lazaric", "bone"]\["t", "r", "l"]\["t", "l"]\["t", "r", "l"]\\[]\\\["Grid"]
2164114810\Cross-domain transfer for reinforcement learning\Some("https://www.researchgate.net/profile/Matthew_Taylor12/publication/221345060_Cross-domain_transfer_for_reinforcement_learning/links/0fcfd5114478d8d544000000.pdf")\["a", "r", "v"]\["diff-it"]\["rule", "advisor"]\["j", "tt", "tr"]\\["Custom", "CS", "Figures", "Formulas", "Pseudo", "Tables"]\["Q"]\["sup"]\0\0\\["Simulation", "RoboCup", "2D", "Navigation", "Grid", "KnightJoust", "Ringworld", "MultiAgent", "KeepAway", "3v2"]\["TD", "any"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor", "mag", "bone"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\[["Ringworld", "KeepAway3v2"], ["KnightJoust", "KeepAway3v2"]]\\\
2079247031\Autonomous shaping: knowledge transfer in reinforcement learning\Some("http://portal.acm.org/citation.cfm?doid=1143844.1143906")\["p"]\["diff-no"]\["r"]\["j", "tr"]\\["Custom", "CS", "Figures", "Formulas"]\["SARSA"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Homing"]\["TD"]\1\0\0\["USA"]\["University of Massachusetts"]\["Computer Science"]\0\["h"]\["taylor", "mag", "lazaric", "taylor-intertask", "bone", "silva", "zhu"]\["t", "r", "l", "k"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\["Obstacles"]
2795717084\StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning\Some("http://export.arxiv.org/abs/1804.00810")\["s_i", "s_f", "levels", "t"]\["multi"]\["curriculum"]\["j", "tr", "tt", "ap"]\\["Custom", "OSS", "bwapi", "Pseudo", "Figures", "Formulas", "Tables"]\["PS-MAGDS", "SARSA"]\["N/A"]\0\1\\["Simulation", "Games", "VideoGames", "StarCraft", "Micromanagement", "Curriculum", "MultiAgent"]\["TD", "H", "MB"]\1\0\0\["China"]\["Chinese Academy of Sciences", "State Key Laboratory of Management and Control for Complex Systems"]\["Institute of Automation"]\0\["all"]\["mag", "cur"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2126565096\Behavior transfer for value-function-based reinforcement learning\Some("http://dl.acm.org/citation.cfm?doid=1082473.1082482")\["v", "#"]\["diff-it"]\["Q"]\["j", "tt"]\\["Custom", "CS", "RoboCup", "Formulas", "Videos", "Figures", "Tables"]\["SARSA", "CMAC", "SMDP"]\["sup"]\0\0\\["2D", "Simulation", "RoboCup", "KeepAway", "MultiAgent", "4v3", "3v2"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Compouter Sciences"]\0\["h"]\["mag", "lazaric", "cur"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["KeepAway3v2", "KeepAway4v3"]]\\\
2950819666\Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control\Some("http://export.arxiv.org/abs/1812.03216")\["t", "s_i", "s_f", "s", "levels"]\["diff-no"]\["fea", "pi"]\["ap", "tr", "j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables", "Videos"]\["RL-RC"]\["N/A"]\0\1\\["Robotics", "Autonomous", "Driving", "Vehicle", "Simulation"]\["H", "TD"]\1\0\0\["USA"]\["University of California", "Berkeley DeepDrive"]\["Mechanical Engineering"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2795341696\Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system\Some("https://au.arxiv.org/pdf/1803.10371")\["t"]\["sim2real"]\["pi"]\["j"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Videos", "Tables"]\["NPG", "PG"]\["N/A"]\0\1\\["Robotics", "3D", "Arm", "Simulation", "RealWorld", "Push"]\["TD"]\1\0\0\["USA"]\["University of Washington", "Roboti LLC"]\["Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2114580749\An Introduction to Intertask Transfer for Reinforcement Learning\Some("http://www.aaai.org/ojs/index.php/aimagazine/article/view/2329")\[]\["theory", "survey"]\[]\[]\\[]\[]\[]\2\2\\["theory", "survey"]\[]\1\0\0\["USA"]\["Lafayette College", "The University of Texas"]\["Computer Science"]\0\[]\["mag", "multi"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "s", "k"]\\\\\
2153353285\Using advice to transfer knowledge acquired in one reinforcement learning task to another\Some("https://doi.org/10.1007/11564096_40")\["p"]\["diff-it"]\["advisor"]\["ap", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["SARSA"]\["sup"]\0\0\\["2D", "RoboCup", "KeepAway", "BreakAway", "MultiAgent", "Simulation", "3v2", "2v1"]\["TD"]\1\0\0\["USA"]\["University of Wisconsin", "University of Minnesota"]\[]\0\["h"]\["taylor", "lazaric", "zhu", "mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\[["BreakAway", "KeepAway"]]\\\
2124695578\Transfer learning for reinforcement learning on a physical robot\Some("https://www.researchgate.net/profile/Matthew_Taylor12/publication/228959234_Transfer_learning_for_reinforcement_learning_on_a_physical_robot/links/0fcfd51144740519c6000000.pdf?origin=publication_detail")\["t"]\["sim2real", "multi"]\["Q"]\["j", "tr", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Web", "Dead"]\["Q", "SARSA"]\["sup"]\0\0\\["3D", "Robotics", "KeepAway", "RoboCup", "Simulation", "RealWorld", "MultiAgent"]\["TD"]\1\0\0\["USA"]\["The University of Texas", "University of Southern California"]\["Computer Science"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2004030284\Transfer of samples in batch reinforcement learning\Some("https://core.ac.uk/display/108628857")\["s_i", "s_f", "s"]\["multi"]\["I"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["FQI"]\["N/A"]\0\0\\["2D", "Navigation", "Obstacle", "Samples", "Simulation"]\["TD", "batch"]\1\1\0\["Italy"]\["Politecnico di Milano"]\["IIT-Lab"]\0\["lib"]\["lazaric", "zhu", "bone", "mag", "cur"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Areas", "Obstacles"]
2110292307\Transferring instances for model-based reinforcement learning\Some("https://rd.springer.com/chapter/10.1007/978-3-540-87481-2_32")\["a", "v"]\["diff-it", "lit"]\["I"]\["ap", "tr", "j"]\\["Custom", "CS", "Pseudo", "Web", "Dead", "Figures", "Formulas", "Tables"]\["SARSA", "R-Max", "TIMBREL"]\["sup", "exp"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "ClassicControl"]\["MB", "all"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor", "lazaric", "taylor-intertask", "bone",  "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["MountainCar", "MountainCar3D"]]\\\
2154328025\Transfer via inter-task mappings in policy search reinforcement learning\Some("http://www.cs.utexas.edu/users/ai-lab/?taylor:ijcaams07")\["a", "v"]\["diff-it", "lit"]\["pi"]\["tt", "j", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["TVITM-PS"]\["sup"]\0\0\\["Simulation", "RoboCup", "KeepAway", "MultiAgent", "3v2", "4v3"]\["PS", "all"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor", "silva", "bone", "taylor-intertask", "lazaric",  "mag", "cur"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["KeepAway3v2", "KeepAway4v3"]]\\\
2158150115\Autonomous transfer for reinforcement learning\Some("https://dl.acm.org/citation.cfm?id=1402427")\["a", "v", "t"]\["lit", "diff-no"]\["I", "Q"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables", "Web", "Dead"]\["Q"]\["exp"]\1\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "Handbrake", "ClassicControl"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["lazaric", "bone", "cur", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[["MountainCar", "MountainCar3D"]]\\\
2913485808\Universal Successor Features for Transfer Reinforcement Learning\Some("http://arxiv.org/pdf/2001.04025.pdf")\["s_i", "s_f", "levels", "r"]\["multi"]\["fea"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Gym"]\["DQN", "DDPG", "USF"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Reacher", "FetchReach", "Simulation"]\["TD"]\1\0\0\["USA", "Canada"]\["University of Alberta", "University of Montreal"]\["Computer Science"]\0\["all"]\["zhu", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
2784831250\Cross-Domain Transfer in Reinforcement Learning using Target Apprentice\Some("http://export.arxiv.org/pdf/1801.06920")\["v", "a", "t"]\["diff-no", "lit"]\["I", "advisor"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Theorem"]\["FQI", "Q", "UMA", "TA"]\["Ma", "exp"]\1\0\\["2D", "Simulation", "CartPole", "Bicycle", "Navigation", "Grid", "ClassicControl"]\["TD", "batch"]\1\0\0\["USA"]\["University of Illinois"]\["Department of Agriculture and Biological Engineering and Coordinated Science Lab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\[["CartPole", "Bicycle"]]\\\
2949876402\Grounding Language for Transfer in Deep Reinforcement Learning\Some("http://export.arxiv.org/abs/1708.00133")\["t", "s"]\["multi"]\["pi"]\["ap", "tr"]\\["OSS", "Custom", "Pseudo", "Formulas", "Figures", "Tables"]\["DQN", "AMN", "VIN"]\["sup", "text"]\0\1\\["GVGAI", "Simulation", "Games", "MonteCarlo"]\["TD"]\1\0\0\["USA"]\["Princeton University", "Massachusetts Institute of Technology"]\["Computer Science", "Artificial Intelligence Laboratory"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\["Grid", "Obstacles"]
2132057084\Transfer in variable-reward hierarchical reinforcement learning\Some("https://link.springer.com/content/pdf/10.1007/s10994-008-5061-y.pdf")\["r"]\["multi"]\["pi"]\["tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Theorem"]\["MAXQ", "VRHRL"]\["N/A"]\0\0\\["RTS", "2D", "Grid", "Simulation"]\["H", "MB"]\1\0\0\["USA"]\["Oregon State"]\["Electrical Engineering and Computer Science"]\0\["lib"]\["taylor", "lazaric", "zhu", "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\\
36691172\Using Homomorphisms to transfer options across continuous reinforcement learning domains\Some("https://dl.acm.org/citation.cfm?id=1597538.1597618")\["a", "v"]\["lit"]\["N/A"]\["ap", "j", "tr"]\\["Custom", "CS",  "Pseudo", "Figures", "Formulas"]\["Q", "IntraOptionQ", "CMAC"]\["Ma", "svg", "exp"]\0\0\\["2D", "Navigation", "Blocksworld", "KeepAway", "RoboCup", "Simulation", "MultiAgent", "3v2", "4v3"]\["all", "H"]\1\0\0\["USA"]\["University of Michigan"]\["Computer Science and Engineering"]\0\["h"]\["taylor", "taylor-intertask", "lazaric", "cur", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\[["Blocksworld3", "Blocksworld7"],["Blocksworld4", "Blocksworld7"],["Blocksworld5", "Blocksworld7"],["Blocksworld6", "Blocksworld7"], ["KeepAway3v2", "KeepAway4v3"]\\\["Blocksworld",]
2925000324\Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus\Some("https://ui.adsabs.harvard.edu/abs/2019arXiv190310671G/abstract")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures", "Formulas", "Tables"]\[]\[]\0\1\\["NLP", "Simulation", "Text", "StyleTransfer"]\[]\1\0\0\["USA"]\["University of Illinois", "T. J. Watson Research Center"]\["Industry", "Uni", "Colab"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
1848094219\Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment\Some("https://dl.acm.org/citation.cfm?id=2886521.2886669")\["t", "a", "v"]\["diff-no", "lit"]\["I", "pi", "manifold"]\["j", "tt", "ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["PG", "MAXDT-PG"]\["exp"]\1\0\\["2D", "CartPole", "SimpleMass", "CartPoleThreeLink", "Simulation", "ClassicControl"]\["TD"]\1\0\0\["USA"]\["University of Pennsylvania", "Olin College of Engineering", "Washington State University"]\[]\0\["h"]\["mag", "zhu"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["SimpleMass", "CartPole"], ["CartPole", "ThreeLinkCartPole"], ["CartPole", "Quadrotor"]]\\\
110451278\Reinforcement learning transfer via sparse coding\Some("https://cris.maastrichtuniversity.nl/portal/en/publications/reinforcement-learning-transfer-via-sparse-coding(55f157dc-7588-4af7-9d75-bf299bd3e19e).html")\["t", "a", "v"]\["diff-no", "lit"]\["Q", "I"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["LSPI", "FQI"]\["exp"]\0\0\\["2D", "Simulation", "MountainCar", "InvertedPendulum", "CartPole", "ClassicControl"]\["TD", "batch"]\1\0\0\["Netherlands", "USA"]\["Maastricht University", "Lafayette College"]\[]\126751897\["h"]\["zhu", "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["InvertedPendulum", "MountainCar"], ["MountainCar", "CartPole"]]\\1\
2098723043\Value-function-based transfer for reinforcement learning using structure mapping\Some("https://www.aaai.org/Library/AAAI/2006/aaai06-066.php")\["a", "v"]\["lit"]\["N/A"]\[]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["QDBN"]\["exp"]\0\0\\["2D", "RoboCup", "BreakAway", "Simulation", "Games", "MultiAgent", "Qualitative Dynamic Bayes Network", "3v2", "4v3"]\["all"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor", "taylor-intertask", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[]\\\
2281071090\Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning\Some("http://www.sciencedirect.com/science/article/pii/S0378778816300305")\["t"]\["same_all"]\["Q"]\["ap", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["SARSA", "DBN", "Q"]\["N/A"]\0\1\\["Deep Belief Network","Power", "Efficiency", "SmartGrid", "RBM", "Simulation"]\["TD"]\1\0\0\["Netherlands"]\["Technical University of Eindhoven"]\["Electrical Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\
2963983495\Theoretically-grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer\Some("https://dl.acm.org/citation.cfm?id=3060945")\[]\["same_all"]\["advisor"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Theorem", "Lemma"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Grid", "CombinationLock", "Maze", "BlockDude"]\["TD"]\1\0\0\["USA"]\["Washington State University", "Princeton University"]\[]\0\[]\["mag", "silva"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2235081654\Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning\Some("http://www.seas.upenn.edu/~eeaton/papers/BouAmmar2015Autonomous.pdf")\["a", "p", "r", "t", "v"]\["lit", "multi"]\["sub", "fea", "pi"]\["j"]\\["Custom", "CS",  "Formulas", "Theorem", "Figures"]\["Cross-Domain", "PG-Ella", "PG"]\["exp"]\1\0\\["Cross-Domain", "Lifelong", "PolicyGradient", "SimpleMass", "DoubleMass", "CartPole", "DoubleCartPole", "Bicycle", "Helicpoter", "Simulation", "ClassicControl"]\["TD"]\1\0\0\["USA"]\["University of Pennsylvania", "Olin College of Engineering"]\["Computer Science"]\0\["all"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[["Many", "Many"]]\\\
2341634245\Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning.\Some("https://philpapers.org/rec/COLNSO")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\1\0\0\["USA"]\["Brown University", "University of California Berkeley"]\["Cognitive, Linguistic and Psychological Science", "Psychology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2141559023\An automated measure of MDP similarity for transfer in reinforcement learning\Some("http://www.seas.upenn.edu/~eeaton/papers/BouAmmar2014Automated.pdf")\["t"]\["multi"]\["Q"]\["j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["SARSA", "Q", "FQI", "LSPI"]\["N/A"]\0\1\\["2D", "Simulation", "CartPole", "InvertedPendulum", "MountainCar", "RBDist", "ClassicControl"]\["TD", "batch"]\1\0\0\["USA", "Netherlands", "UK"]\["University of Pennsylvania", "Washington State University", "Technical University of Eindhoven", "University of Maastricht", "University of Liverpool"]\[]\0\["lib", "h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2604618034\Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay.\Some("https://dr.ntu.edu.sg/handle/10220/42453")\["t", "s_i", "s_f", "levels", "s"]\["multi"]\["fea", "pi_dyn", "distil"]\["ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["DQN", "AMN", "DIST"]\["N/A"]\0\1\\["2D", "Simulation", "Games", "VideoGames", "Atari"]\["TD", "H"]\1\0\0\["Singapore"]\["Nanyang Technological University"]\["Computer Science and Engineering"]\0\["lib"]\["mag", "zhu"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2972430483\Transfer of Temporal Logic Formulas in Reinforcement Learning.\Some("https://arxiv.org/abs/1909.04256")\["s_i", "s_f", "levels"]\["multi"]\["Q"]\["tt", "tr", "ap"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Timed", "Simulation"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Computational Engineering and Sciences", "Aerospace Engineering and Engineering Mechanics"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2586101872\An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems\Some("https://doi.org/10.1109/TEVC.2017.2664665")\["s_i", "s_f", "levels"]\["multi"]\["Q", "advisor"]\["ap"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["Q", "FALCON", "BP"]\["N/A"]\0\1\\["2D", "3D", "Simulation", "Games", "Navigation", "Minefield", "MinefieldNavigation", "UnrealTournament", "MultiAgent"]\["TD"]\1\1\1\["Singapore", "China", "USA"]\["Nanyang Technological University", "Chongqing University", "University of Louisville"]\["Computer Science and Engineering", "Electrical and Computer Engineering"]\2778259398\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\3\
2807652507\Context-Aware Indoor VLC/RF Heterogeneous Network Selection: Reinforcement Learning With Knowledge Transfer\Some("https://dblp.uni-trier.de/db/journals/access/access6.html#DuWSW18")\["t"]\["same_all"]\["Q"]\["tt", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q" ]\["exp"]\0\0\\["Indoor Network Selection", "Network", "Contextaware", "Simulation", "MonteCarlo"]\["TD"]\1\0\0\["China"]\["National University of Defense Technology", "National Digital Switching System Engineering and Technological Research Center"]\["Information and Communcation"]\0\["lib"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[]\\\
2259258048\Multiagent Reinforcement Learning With Sparse Interactions by Negotiation and Knowledge Transfer\Some("https://www.ncbi.nlm.nih.gov/pubmed/27046917")\["s", "levels"]\["multi"]\["Q"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q", "CQ", "NegoQV", "ILVFT", "NegoSI"]\["N/A"]\0\0\\["2D", "Simulation", "Navigation", "Grid", "Warehouse", "MultiAgent"]\["TD"]\1\0\0\["China", "USA"]\["Nanjing University", "University of Michigan"]\["Control and Systems Engineering", "State Key Laboratory for Novel Software Technology"]\0\["h"]\["silva", "mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
1533597678\Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling\Some("https://doi.org/10.1007/978-3-540-74958-5_70")\["#"]\["diff-no"]\["Q"]\["ap", "j", "tt", "tr"]\\["Custom", "CS", "Figures"]\["TgR"]\["N/A"]\0\0\\["Bongard", "Blocksworld", "TicTacToe", "2D", "Navigation", "BoardGames", "Simulation", "Games"]\["RRL"]\1\0\0\["Netherlands"]\["K.U.Leuven"]\["Computer Science"]\0\[]\["taylor", "bone", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[]\\\["Blocksworld"]
2156493855\Relational macros for transfer in reinforcement learning\Some("https://experts.umn.edu/en/publications/relational-macros-for-transfer-in-reinforcement-learning")\["a", "r", "v"]\["diff-it"]\["relational-macros"]\["j", "tr"]\\["Custom", "CS", "Pseudo", "Figures", "Tables"]\["Q", "RMT-D"]\["sup"]\0\0\\["2D", "RoboCup", "BreakAway", "Simulation", "Games", "MultiAgent", "2v1", "3v2", "4v3"]\["TD", "RRL"]\1\0\0\["USA"]\["University of Minnesota", "University of Wisconsin"]\["Computer Science"]\0\["h"]\["taylor", "taylor-intertask", "bone", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\[]\\\
2953052971\Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language\Some("https://export.arxiv.org/pdf/1808.06167")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures"]\["REINFORCE"]\[]\0\1\\["Translation", "Simulation", "NLP"]\["TD", "PS"]\1\0\0\["China", "US"]\["National Laboratory of Pattern Recognition", "University of Chinese Academy of Sciences", "Mobvoi AI Lab"]\["Institute of Automation", "Industry"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2766253973\Heuristically Accelerated Reinforcement Learning by Means of Case-Based Reasoning and Transfer Learning\Some("https://researchnow.flinders.edu.au/en/publications/heuristically-accelerated-reinforcement-learning-by-means-of-case")\["t"]\["sim2real"]\["pi", "Q", "cases"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["Q", "HAQL", "TLHAQL"]\["N/A"]\0\0\\["Robotics", "Simulation", "RealWorld", "RoboCup", "Kilobot", "Acrobot", "MultiAgent", "ClassicControl"]\["CBR", "TD"]\1\0\0\["Brazil", "Spain"]\["Technological Institute of Aeronautics", "Spanish National Research Council", "Centro Universitário da FEI"]\["Artificial Intelligence Research Institute"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\[["RoboCup", "Kilobot"], ["Acrobot", "Humanoid"]]\\\
2960705509\Assessing Transferability from Simulation to Reality for Reinforcement Learning\Some("https://export.arxiv.org/pdf/1907.04685")\["s_i", "s_f", "t"]\["sim2real", "multi"]\["pi"]\["ap"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables", "Videos"]\["SPOTA", "EPOpt", "PPO"]\["N/A"]\0\1\\["3D", "Robotics", "BallBalancer", "CartPole", "SimulationOptimizationBias", "Simulation", "RealWorld", "ClassicControl"]\["TD", "PS"]\1\0\0\["Germany"]\["Technische Universität Darmstadt", "Honda Research Institute Europe", "Max Planck Institute"]\["Intelligent Autonomous Systems Group", "Industry"]\0\["h"]\["mag"]\["t", "r", "l"]\["t"]\["t", "r", "l"]\\\\\
2785494456\Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning\Some("https://export.arxiv.org/pdf/1806.04798")\[]\["pure"]\["DNN"]\[]\\["Custom", "CS", "Figures", "Formulas", "Tables"]\["SingleRL", "MLP-GAL", "REINFORCE"]\["N/A"]\0\1\\["Meta", "Simulation", "Datasets", "UCI"]\["TD", "PS"]\1\0\0\["UK", "Japan"]\["University of Edinburgh", "University College London", "Nara Institute of Technology"]\["Institute of Perception, Action and Behaviour", "Statistics"]\0\[]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l"]\\\2\\
2534140487\Using Transfer Learning to Speed-Up Reinforcement Learning: A Cased-Based Approach\Some("https://www.iiia.csic.es/es/publications/using-transfer-learning-speed-reinforcement-learning-case-based-approach")\["v", "t"]\["diff-it"]\["cases", "pi", "Q"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q", "HAQL", "TL-HAQL"]\["sup"]\0\1\\["2D", "3D", "2Dto3D", "MountainCar", "Simulation", "ClassicControl"]\["CBR", "TD"]\1\0\0\["Brazil", "Spain"]\["Technological Institute of Aeronautics", "Spanish National Research Council", "Centro Universitário da FEI"]\["Artificial Intelligence Research Institute"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["MountainCar", "MountainCar3D"]]\\\
2997770298\Correction: Guidance for Closed-Loop Transfers using Reinforcement Learning with Application to Libration Point Orbits\Some("http://dx.doi.org/10.2514/6.2020-0458.c1")\[]\["pure"]\[]\[]\\["Custom", "CS", "PPO-Pat-Coady", "Formulas", "Figures", "TensorFlow", "Tables"]\["PPO"]\[]\0\1\\["Simulation", "Space", "ClosedLoop", "SpaceCraft"]\["TD"]\1\0\0\["USA"]\["Purdue University", "Massachusetts Institute of Technology"]\[]\0\[]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
2036103676\Accelerating Multiagent Reinforcement Learning by Equilibrium Transfer\Some("http://or.nsfc.gov.cn/handle/00001903-5/480512")\["r", "s", "#", "v"]\["multi", "multiagent"]\["equilibrium"]\["j", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Theorem", "Lemma", "Figures", "Tables"]\["NashQ", "CEQ"]\[]\0\0\\["2D", "Grid", "Navigation", "Simulation", "SoccerGame", "WallGame", "MultiGrid", "Equilibrium", "MultiAgent"]\["TD"]\1\0\0\["China", "Singapore"]\["Nanjing University", "Nanyang Technological University"]\["Computer Science", "Computer Engineering"]\0\[]\["mag", "silva"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\["Grid", "WallGame", "Soccergame"]
2907543530\Deep Reinforcement Learning with Knowledge Transfer for Online Rides Order Dispatching\Some("https://dblp.uni-trier.de/db/conf/icdm/icdm2018.html#WangQTYZ18")\["t", "s_i", "s_f", "levels"]\["multi"]\["fea"]\["j", "tr", "tt", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["DQN"]\["N/A"]\0\1\\["Simulation", "RideSharing", "Dispatch", "MultiAgent"]\["TD"]\1\1\1\["USA", "China"]\["Washington State University", "DiDi Research America", "DiDi Chuxing"]\["Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2014512216\Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty\Some("https://www.researchgate.net/profile/Michael_Madden3/publication/220637641_Transfer_of_Experience_Between_Reinforcement_Learning_Environments_with_Progressive_Difficulty/links/00b7d5249d3c37fa43000000.pdf?disableCoverPage=true")\["s_i", "s_f", "t", "levels"]\["multi"]\["rule"]\["tr", "tt"]\\["Custom", "CS", "Figures", "Formulas", "Tables"]\["Q", "P-RL"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["TD"]\1\0\0\["Ireland"]\["National University of Ireland"]\["Information Technology"]\0\["all"]\["taylor", "silva", "bone", "lazaric", "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\\["Grid", "Maze"]
2742143911\Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning\Some("http://export.arxiv.org/pdf/1708.00102")\["s_i", "s_f", "r"]\["multi"]\["fea", "successor"]\["j", "tt", "ap"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "Tables", "TensorFlow"]\["FQI", "FSF"]\["N/A"]\0\1\\["Simulation", "2D", "Navigation", "Grid", "Successor"]\["TD", "batch"]\1\0\0\["USA"]\["Brown University"]\[]\0\["all"]\["zhu", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
2963049105\Online Transfer Learning in Reinforcement Learning Domains.\Some("http://irll.eecs.wsu.edu/wp-content/papercite-data/pdf/2015sdmia-zhan.pdf")\["s"]\["same_all"]\["advisor"]\["tr"]\\["CS", "Custom", "Theorem", "Figures", "Tables", "Formulas", "Lemma"]\["Q", "SARSA"]\["N/A"]\0\0\\["Simulation", "Games", "Pacman", "Atari"]\["TD"]\1\0\0\["USA"]\["Washington State University"]\["Electrical Engineering and Computer Science"]\0\["h"]\["zhu", "mag"]\["t", "r", "l"]\["t", "l"]\["t", "r", "l", "k"]\\\\\
2985293964\Transfer in Deep Reinforcement Learning Using Knowledge Graphs.\Some("https://dblp.uni-trier.de/db/journals/corr/corr1908.html#abs-1908-06556")\["s", "levels"]\["multi", "lit"]\["graph", "pi"]\["tt"]\\["CS", "Custom", "KG-DQN", "PyTorch", "Jericho", "TextWorld", "Formulas", "Figures", "Tables"]\["DQN", "KG-DQN"]\["N/A"]\0\1\\["Simulation", "TextBased", "Game"]\["TD"]\1\0\0\["USA"]\["Georgia Institute of Technology"]\["Interactive Computing"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2771643225\Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning\Some("http://export.arxiv.org/abs/1712.04516")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["A3C"]\["N/A"]\0\1\\["Chemistry", "Simulation", "CNN"]\["TD"]\1\0\0\["USA"]\["Carnegie Mellon University"]\["Chemistry,", "Computer Science"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2949472713\Transfer and Online Reinforcement Learning in STT-MRAM Based Embedded Systems for Autonomous Drones\Some("https://arxiv.org/abs/1905.06314")\["s_i", "s_f", "levels"]\["multi"]\["fea", "pi"]\["ap"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "TensorFlow", "AirSim", "Videos", "LinkNotIncluded"]\["DDQN", "DNN"]\["N/A"]\0\1\\["RealTime", "Simulation", "Drone", "ObjectAvoidance", "CNN", "STT-MRAM", "Robotics", "Vehicle", "Aerial"]\["TD"]\1\0\0\["USA"]\["Georgia Institute of Technology", "Samsung semiconductor"]\["Advanced Logic Lab"]\2966501669\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\2\
2145729962\Empirical Evidence of Priming  Transfer  Reinforcement  and Learning in the Real and Virtual Trillium Trails\Some("https://doi.org/10.1109/TLT.2010.20")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\1\0\0\["USA"]\["University of Central Florida"]\["Psychology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2782656435\Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors.\Some("http://export.arxiv.org/pdf/1801.02268")\["s_i", "s_f", "levels"]\["multi"]\["pri", "fea"]\["tr"]\\["Custom", "CS", "TensorFlow", "Keras", "Gym", "Figures", "Formulas"]\["DQN", "DDQN"]\["N/A"]\0\1\\["2D", "Simulation", "Navigation", "Grid", "Pickup"]\["TD"]\1\0\0\["USA"]\["Cornell University"]\["SE(3) Group"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\2\\
2953431737\End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer\Some("https://dblp.uni-trier.de/db/journals/ras/ras119.html#YuanHKWS19")\["t", "s_i", "s_f", "#"]\["sim2real", "multi"]\["pi", "fea"]\["ap"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "Tables"]\["DQN", "Q"]\["N/A"]\0\1\\["Simulation", "RealWorld", "Robotics", "Arm", "Objects", "Obstacle"]\["TD"]\1\1\1\["China", "USA", "Sweden"]\["Hong Kong University of Science and Technology", "Yale University", "KTH Royal Institute of Technology", "Örebro University"]\["Robotics Institute", "Mechanical Engineering and Material Science", "Centre for Autonomous System", "Centre for Applied Autonomous Sensor Systems"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2138497321\Representation Transfer for Reinforcement Learning\Some("http://www.cs.utexas.edu/users/ai-lab/?AAAI07-Symposium-Taylor")\["a", "v"]\["diff-it"]\["Q"]\["tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["SARSA"]\["sup"]\0\0\\["Simulation", "RoboCup", "KeepAway", "MultiAgent", "3v2", "4v3"]\["TD", "PS"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor", "mag", "lazaric", "zhu", "bone"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[["KeepAway3v2", "KeepAway4v3"]]\\\
2289068127\Don't think  just feel the music: Individuals with strong pavlovian-to-instrumental transfer effects rely less on model-based reinforcement learning\Some("http://pesquisa.bvsalud.org/portal/resource/pt/mdl-26942321")\[]\["pure"]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\1\0\0\["USA"]\[]\["Psychology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2963913081\Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation\Some("http://proceedings.mlr.press/v97/gamrian19a.html")\["s", "t", "s_i", "s_f", "levels"]\["diff-no"]\["GAN", "imitation"]\["ap", "tt"]\\["OSS", "Custom", "PyTorch", "Gym", "Pseudo", "Formulas", "Figures", "Tables", "Videos"]\["A2C", "A3C"]\["N/A"]\0\1\\["2D", "Games", "VideoGames", "Atari", "Breakout", "Roadfighter", "Simulation"]\["TD"]\1\0\0\["Israel"]\["Bar-Ilan University", "Allen Institute for Artificial Intelligence"]\["Computer Science", "Industry"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
1457482454\Transfer learning in multi-agent reinforcement learning domains\Some("https://doi.org/10.1007/978-3-642-29946-9_25")\["#", "s_i", "s_f"]\["multi"]\["Q", "pi"]\["j", "ap"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "Tables"]\["Q", "BITER"]\["Ma", "exp"]\0\0\\["2D", "Navigation", "Grid", "Simulation", "PredatorPrey", "MultiAgent"]\["TD"]\1\0\0\["Greece"]\["Aristotle University"]\["Informatics"]\0\["h"]\["silva", "mag", "multi"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["PredatorPrey"]
2739691807\Deep Transfer in Reinforcement Learning by Language Grounding.\Some("http://export.arxiv.org/abs/1708.00133")\["t", "s"]\["multi"]\["pi"]\["ap", "tr"]\\["OSS", "Custom", "Pseudo", "Formulas", "Figures", "Tables"]\["DQN", "AMN", "VIN"]\["sup", "text"]\0\1\\["GVGAI", "Simulation", "Games", "CNN", "NLP", "MonteCarlo"]\["TD", "MB"]\1\0\0\["USA"]\["Princeton University", "Massachusetts Institute of Technology"]\["Computer Science", "Artificial Intelligence Laboratory"]\2949876402\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\4\
3001528895\Single Episode Policy Transfer in Reinforcement Learning.\Some("http://arxiv.org/pdf/1910.07719.pdf")\["t"]\["multi"]\["VAE", "latentspace"]\["tt", "tr"]\\["OSS", "Custom", "Pseudo", "Formulas", "Figures"]\["DDQN"]\["N/A"]\0\1\\["Simulation", "2D", "Navigation", "Acrobot", "HIV", "ClassicControl"]\["TD", "MB"]\1\0\0\["USA"]\["Georgia Institute of Technology", "Lawrence Livermore National Laboratory"]\[]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2887671224\Policy and Value Transfer in Lifelong Reinforcement Learning\Some("http://proceedings.mlr.press/v80/abel18b.html")\["v", "s_i", "s_f", "levels"]\["multi"]\["Q"]\["j", "tr", "tt", "ap"]\\["OSS", "Custom", "Pseudo", "Formulas", "Figures", "Theorem", "Tables"]\["Q", "Delayed-Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation"]\["TD", "MB"]\1\0\0\["USA"]\["Brown University"]\["Computer Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "l"]\["t", "r", "l", "k", "s"]\\\\\
1490954610\Learning relational options for inductive transfer in relational reinforcement learning\Some("https://rd.springer.com/chapter/10.1007/978-3-540-78469-2_12")\["#"]\["diff-no"]\["options"]\["ap", "j", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["Q", "TILDE"]\["N/A"]\0\0\\["2D", "Navigation", "TicTacToe", "BoardGames", "Blocksworld", "Grid", "Simulation", "Games"]\["RRL", "H"]\1\1\1\["Netherlands"]\["K.U.Leuven"]\["Computer Science"]\0\["h"]\["taylor", "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\["Blocksworld"]
2979341666\Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning\Some("https://arxiv.org/abs/1910.05547")\["t", "s_i", "s_f", "levels"]\["sim2real", "multi"]\["pi", "offline"]\["j", "tr", "ap", "tt"]\\["OSS", "TensorFlow", "AirSim", "Figures", "Tables", "Formulas", "Pseudo", "Videos"]\["DQN"]\["N/A"]\0\1\\["3D", "Navigation", "Simulation", "Drone", "RealWorld", "CNN", "Aerial"]\["TD"]\1\0\0\["USA"]\["Georgia Institute of Technology"]\["Electrical and Computer Engineering"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2921955147\A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems\Some("https://doi.org/10.1613/jair.1.11396")\[]\["theory", "survey"]\[]\[]\\[]\[]\[]\2\1\\["theory", "survey", "multiagent"]\[]\1\0\0\["Brazil"]\["University of Sao Paulo"]\["Computer Engineering"]\0\[]\["mag", "zhu", "multi"]\["t", "r", "l"]\["r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2181867278\Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines\Some("https://research.tue.nl/en/publications/automatically-mapped-transfer-between-reinforcement-learning-task-2")\["a", "t", "r"]\["lit"]\["pi"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures"]\["RBM"]\["exp"]\1\1\\["Simulation", "MountainCar", "CartPole", "RBM", "ClassicControl"]\["TD"]\1\0\0\["Netherlands", "USA"]\["Maastricht University", "Washington State University"]\["Knowledge Engineering", "Electrical Engineering and Computer Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["Inverted Pendulum", "CartPole"], ["MountainCar", "CartPole"]]\\\
2313922182\Towards transferring skills to flexible surgical robots with programming by demonstration and reinforcement learning\Some("https://ieeexplore.ieee.org/document/7449855/")\["t"]\["same_all", "sim2real"]\["pi"]\[]\\["Custom", "CS", "Figures", "Formulas"]\["Q", "PoWER"]\["N/A"]\0\0\\["Simulation", "Robotics", "Arm", "Surgery"]\["TD"]\1\1\1\["China", "Singapore"]\["University of Hong Kong", "National University of Singapore"]\["Industrial Manufacturing Systems Engineering", "Biomedical Engineering"]\0\[]\["mag"]\["t", "r", "l", "s"]\["r", "l"]\["r", "l"]\\\\\
2944895663\TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning\Some("http://eprints.gla.ac.uk/212298/")\["t", "a"]\["same_all"]\["model", "latent"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Gym", "rllab", "SAC", "Figures", "Tables", "github/haarnoja/sac"]\["TibGM", "DDPG", "LSP", "SAC", "PPO", "ERL", "DIAYN", "VIREL", "GEP-PG", "ProMP"]\["N/A"]\0\1\\["Simulation", "Robotics", "Mujoco", "Swimmer", "Hopper", "Walker2d",  "HalfCheetah", "Ant", "Humanoid", "MountainCar", "ClassicControl"]\["TD", "H"]\1\0\0\["UK"]\["University of Cambridge", "Alan Turing Institute"]\["Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2735260544\Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning\Some("https://dblp.uni-trier.de/db/journals/aim/aim32.html#MehtaRTD11")\["v", "a"]\["diff-no"]\["model", "hierarchy", "sub"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Figures"]\["Q", "HI-MAT"]\["N/A"]\0\0\\["Simulation", "2D", "Grid", "Navigation", "Resources", "Mining", "Wargus", "Taxi", "MultiAgent"]\["TD", "H", "B"]\1\0\0\["USA"]\["Oregon State University", "Case Western Reserve University"]\[]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2517639096\Graph based skill acquisition and transfer Learning for continuous reinforcement learning domains\Some("http://www.sciencedirect.com/science/article/pii/S0167865516302112")\["s_i", "s_f", "s", "levels"]\["same_all"]\["Q", "graph"]\["tt", "ap", "tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["GSL", "SARSA"]\["N/A"]\0\0\\["Simulation", "2D", "Pinball", "GraphBased"]\["TD", "H"]\1\1\1\["Iran"]\["University of Tehran"]\["Electrical and Computer Engineering"]\0\["all"]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\["t", "r", "l", "s", "k"]\\\\\
2966501669\Hierarchical Memory System With STT-MRAM and SRAM to Support Transfer and Real-Time Reinforcement Learning in Autonomous Drones\Some("https://ui.adsabs.harvard.edu/abs/2019IJEST...9..485Y/abstract")\["s_i", "s_f", "levels"]\["multi"]\["fea", "pi"]\["ap"]\\["Custom", "CS", "Pseudo", "Figures", "TensorFlow", "AirSim", "Tables"]\["DDQN", "DNN"]\["N/A"]\0\1\\["RealTime", "Simulation", "Drone", "ObjectAvoidance", "CNN", "STT-MRAM", "Robotics", "Vehicle", "Aerial", "RealWorld"]\["TD"]\1\1\1\["USA"]\["Georgia Institute of Technology", "IBM T.J. Watson research center", "Samsung Semiconductor"]\["Industry", "Uni", "Colab"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2619275098\Personalizing a Dialogue System with Transfer Reinforcement Learning\Some("https://www.arxiv.org/abs/1610.02891")\["t", "#"]\["same_all"]\["Q"]\["ap", "tr"]\\["Custom", "CS",  "Formulas", "Pseudo", "Figures", "Tables"]\["Q"]\["lib"]\0\0\\["Simulation", "Dialogue", "Personalized", "PETAL", "CoffeeShopping", "NLP"]\["TD"]\1\0\0\["China"]\["Hong Kong University"]\["Science and Technology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
193076044\Using cases as heuristics in reinforcement learning: a transfer learning application\Some("http://digital.csic.es/bitstream/10261/60868/1/IJCAI%202011%20%281211-1217%29.pdf")\["v", "t", "s", "r"]\["diff-it"]\["cases", "pi", "Q"]\["ap"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q", "HAQL", "CB-HAQL", "L3"]\["sup", "exp"]\0\0\\["2D", "3D", "2Dto3D", "Robotics", "Simulation", "Acrobot", "RoboCup3D", "ClassicControl"]\["CBR", "TD"]\1\0\0\["Brazil", "Spain"]\["Technological Institute of Aeronautics",  " Universitat Autonoma de Barcelona"]\["Electrical Engineering", "Artificial Intelligence Research Institute"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["Acrobot", "RoboCup3D"]]\\\
2925306934\How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?\Some("http://ui.adsabs.harvard.edu/abs/2019arXiv190311774V/abstract")\["t"]\["same_all"]\["pi"]\[]\\["Formulas", "OSS", "Docker", "Gym", "Mujoco", "PPO", "Custom", "PyTorch"]\["PPO"]\["N/A"]\0\1\\["2D", "Robotics", "Simulation", "Dart", "Mujoco", "Hopper", "Walker"]\["TD"]\1\0\0\["USA"]\["University of California"]\["Computer Science and Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\2\\
2897005774\Deep Transfer Reinforcement Learning for Text Summarization\Some("https://arxiv.org/abs/1810.06667")\["s", "r", "datasets"]\["multi"]\["pi", "pi_dyn"]\["ap"]\\["Custom", "CS", "Figures", "Formulas", "ROGUE", "Tables"]\["PG", "SelfCriticPolicyGradient"]\["N/A"]\0\1\\["Text", "Summarization", "ROGUE", "NLP", "Simulation", "CNN"]\["TD"]\1\0\0\["USA"]\["Virginia Tech"]\["Discovery Analytics Center"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2605369401\Transfer Reinforcement Learning with Shared Dynamics\Some("https://hal.archives-ouvertes.fr/hal-01548649")\["t" ,"s_i", "s_f", "levels"]\["multi"]\["r"]\["j", "tt", "ap", "tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation"]\["TD"]\1\0\0\["France", "Canada"]\["Orange Labs at Châtillon", "Maluuba at Montréal"]\["Industry"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2998384510\Exploration of Long Time-of-Flight Three-Body Transfers Using Deep Reinforcement Learning\Some("http://dx.doi.org/10.2514/6.2020-0460")\[]\["pure"]\[]\[]\\["Custom", "CS",  "Formulas", "Pseudo", "Figures", "Tables"]\["DDPG"]\[]\2\1\\["Robotics", "Fuel", "Transfer", "Simulation"]\["TD"]\1\1\1\["Japan"]\["The University of Tokyo", "Japan Aerospace Exploration Agency"]\["Industry", "Uni", "Colab"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
3010212570\Exploiting Multi-Task Learning to Achieve Effective Transfer Deep Reinforcement Learning in Elastic Optical Networks\Some("https://www.osapublishing.org/abstract.cfm?uri=OFC-2020-M1B.3")\["#"]\["multi"]\["pi", "fea"]\["j", "tt", "tr", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["A3C"]\["N/A"]\0\1\\["Elastic", "Optical", "Network", "Simulation"]\["TD"]\1\0\0\["USA", "China"]\["University of California", "University of Science and Technology of China"]\[]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2575472443\Transfer learning for multiagent reinforcement learning systems\Some("https://dl.acm.org/citation.cfm?id=3061181")\[]\["theory"]\[]\[]\\[]\[]\[]\2\2\\["Theory", "MultiAgent"]\[]\1\0\0\["Brazil"]\["Universidade de São Paulo"]\["Escola Politécnica"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3010403426\A Reinforcement Learning Based Network Scheduler for Deadline-Driven Data Transfers\Some("https://dblp.uni-trier.de/db/conf/globecom/globecom2019.html#GhosalSSTW19")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\[]\2\0\\["Network", "DataTransfer", "Deadline", "Scheduling", "Simulation"]\["TD"]\1\0\0\["USA"]\["University of California", "Lawrence Berkley National Laboratory"]\["Computer Science"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2403765497\Directed Exploration in Reinforcement Learning with Transferred Knowledge\Some("http://proceedings.mlr.press/v24/mann12a.html")\["s", "s_i", "s_f", "a"]\["multi"]\["Q"]\["j", "tt"]\\["Custom", "CS", "Theorem", "Formulas", "Lemma", "Figures"]\["DelayedQ"]\["Ma"]\0\0\\["Robotics", "Kinematic", "Arm", "Simulation"]\["TD"]\1\0\0\["USA"]\["Texas A&M University"]\["Computer Science and Engineering"]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\
2049423956\Transfer learning with Partially Constrained Models: Application to reinforcement learning of linked multicomponent robot system control\Some("https://dblp.uni-trier.de/db/journals/ras/ras61.html#Fernandez-GaunaLG13")\["v", "t", "r"]\["multi"]\["Q"]\["tt", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Videos"]\["Q"]\["Ma"]\0\0\\["Robotics", "Simulation", "Hose", "PCM", "2D", "Navigation"]\["TD"]\1\1\1\["Spain"]\["University of the Basque Country"]\["Computational Intelligence Group"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2533806771\A Reinforcement Learning Architecture That Transfers Knowledge Between Skills When Solving Multiple Tasks\Some("http://ieeexplore.ieee.org/document/7592409/")\["t", "s", "s_i", "s_f"]\["multi"]\["advisor"]\["tt"]\\["iCub", "Custom", "CS", "Formulas", "Figures", "Tables"]\["TERL"]\["exp"]\1\1\\["Simulation", "2D", "Robotics", "Arm", "3D", "iCub"]\["TD", "H"]\1\0\0\["Singapore", "Italy"]\["Nanyang Technological University", "Laboratory of Computational Embodied Neuroscience"]\["Robotics Research Centre"]\0\["all"]\["mag"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\\\\\
2951871955\Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement\Some("http://export.arxiv.org/pdf/1901.10964")\["s"]\["multi"]\["fea"]\["j", "tt", "tr", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables", "Videos"]\["Q", "SF+GPI-Q", "DQ"]\["N/A"]\0\1\\["3D", "Navigation", "Simulation", "CNN", "Successor", "Collect"]\["TD"]\1\0\0\["USA"]\["DeepMind"]\["Industry"]\0\["lib"]\["mag", "zhu"]\["t", "r", "l"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\\\\\
1582256513\Task similarity measures for transfer in reinforcement learning task libraries\Some("http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1555955")\["s_i", "s_f", "levels"]\["multi"]\["fea"]\["ap"]\\["Custom", "CS", "Figures", "Formulas", "Tables"]\["Q"]\["N/A"]\0\0\\["Simulation", "2D", "Navigation", "Grid", "MovingGoal"]\["TD"]\1\0\0\["USA"]\["Brigham Young University"]\["Computer Science"]\0\["all"]\["taylor", "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\["Grid", "Obstacles"]
2986946894\An Online Search Method for Representative Risky Fault Chains Based on Reinforcement Learning and Knowledge Transfer\Some("https://ieeexplore.ieee.org/document/8890733")\["t"]\["multi"]\["Q"]\["j", "tt", "tr", "ap"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["Simulation", "RealWorld", "Power", "SmartEnergy", "Outage", "Prevention"]\["TD"]\1\1\1\["China", "USA"]\["Tsinghua University", "Argonne National Laboratory", "University of Tennessee"]\["Electrical Engineering", "Electrical Engineering and Computer Sciences"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2989824507\Deep Reinforcement Learning for Transfer of Control Policies\Some("https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59186/V02AT03A003/1069764")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["Rotor", "Control", "Aircraft", "Simulation", "Aerial"]\[]\0\1\0\["USA"]\["Penn State University"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\[]\\[]\\\
2906096117\Transfer Learning Method based on Spreading Activation Model for Reinforcement Learning\Some("https://www.jstage.jst.go.jp/article/jsmermd/2018/0/2018_1A1-C14/_pdf")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["Mobile robot", "Cognitive psychology", "Psychology"]\[]\0\1\0\["Japan"]\["Japan Society of Mechanical Engineers "]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\[]\\\
2803825057\What to Transfer in Lifelong Reinforcement Learning\Some("https://icml.cc/Conferences/2018/AcceptedPapersInitial#402")\[]\[]\[]\[]\\[]\[]\[]\2\2\\[]\[]\0\0\0\[]\[]\[]\2887671224\[]\["mag"]\["t", "r", "l"]\[]\[]\\\\7\
2626333357\Energy saving in heterogeneous cellular network via transfer reinforcement learning based policy\Some("https://ieeexplore.ieee.org/document/7945411/")\["s_i"]\["same_all"]\["pi"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["Actor-Critic"]\["N/A"]\0\0\\["Energy Saving", "Network", "WiFi", "RealWorld", "Simulation"]\["TD"]\1\1\1\["India"]\["Indraprastha Institute of Information Technology"]\["Electronics and Communication Engineering"]\2604038904\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l"]\\[]\\1\
2607014226\Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning\Some("https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7902152")\["s_i", "s_f", "levels"]\["multi"]\["fea", "Q", "predictions"]\["ap", "tr", "tt"]\\["CreateSim", "Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Simulation", "Navigation", "Maze", "Grid"]\["TD", "MB"]\1\0\0\["Canada"]\["University of Lethbridge"]\["Department of Neuroscience"]\0\["h", "lib"]\["mag", "silva"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2979766322\Federated Transfer Reinforcement Learning for Autonomous Driving.\Some("http://arxiv.org/pdf/1910.06001.pdf")\["t"]\["sim2real"]\["pi", "federated"]\["ap", "tr"]\\["Custom", "CS", "fakeOSS", "Stage", "AirSim", "JetsonTX2", "Figures", "Formulas", "Tables"]\["DDPG"]\["N/A"]\0\1\\["Simulation", "RealWorld", "RCCar", "Driving", "Autonomous", "Indoor", "Collision", "Avoidance", "MultiAgent"]\["TD"]\1\0\0\["China"]\["Hong Kong University of Science and Technology", "WeBank"]\["Industry", "Uni", "Colab", "Robotics and MultiPerception Laborotary", "Department of Artificial Intelligence"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
2965033324\A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer\Some("https://doi.org/10.24963/ijcai.2019/711")\[]\["pure"]\[]\[]\\["OSS", "Github", "TensorFlow", "OpenNMT", "Figures", "Formulas", "Pseudo", "Tables"]\["DualRL"]\[]\2\1\\["StyleTransfer", "Text", "NLP", "Simulation"]\["TD"]\1\0\0\["China"]\["WeChat AI", "Tencent"]\["Industry"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\
2027020365\Automated Transfer for Reinforcement Learning Tasks\Some("https://core.ac.uk/display/153339942")\[]\["theory"]\[]\[]\\[]\[]\[]\2\2\\["Theory"]\[]\1\0\0\["USA", "Netherlands", "UK"]\["University of Pennsylvania", "Maastricht University", "University of Liverpool"]\["Computer and Information Science", "Knowledge Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
1510402218\Using Options for Knowledge Transfer in Reinforcement Learning\Some("https://www.researchgate.net/profile/Doina_Precup/publication/2609073_Using_Options_for_Knowledge_Transfer_in_Reinforcement_Learning/links/0912f51191d83cea16000000.pdf")\["t"]\["multi"]\["options"]\["tt"]\\["Custom", "CS", "Formulas", "Figures"]\["Q" ]\["N/A"]\0\0\\["MountainCar", "Simulation", "ClassicControl"]\["TD", "H"]\1\0\0\["USA"]\["University of Massachusetts"]\[]\0\["all"]\["taylor", "lazaric", "mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\\
2899403804\Design of Transfer Reinforcement Learning Under Low Task Similarity\Some("http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2713207")\["s", "levels"]\["multi"]\["advisor"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "TensorFlow", "Tables"]\["DQN"]\["N/A"]\0\1\\["2D", "Simulation", "Navigation", "Collision", "Avoidance"]\["TD"]\1\0\0\["USA"]\["University of Southern California"]\["Aerospace & Mechanical Engineering"]\3010746488\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "s", "k"]\\\\5\
1523133203\Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions\Some("https://dl.acm.org/citation.cfm?id=142042")\["r"]\["multi"]\[]\["j"]\\["Custom", "CS"]\["Q"]\[]\2\2\\["2D", "Grid", "Navigation", "Robotics", "Simulation"]\["TD"]\0\1\0\["USA"]\["IBM"]\["Industry"]\0\[]\["mag"]\["t", "r", "l"]\[]\[]\\\\\
2110064866\Transferring Expectations in Model-based Reinforcement Learning\Some("https://www.researchgate.net/profile/Tze-Yun_Leong/publication/264503330_Transferring_Expectations_in_Model-based_Reinforcement_Learning/links/53e1f85d0cf2d79877a9f9b4.pdf?origin=publication_detail")\["s_f", "t"]\["multi"]\["model"]\["tr", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["DBN"]\["N/A"]\0\1\\["Simulation", "Expectations", "Views", "2D", "Grid", "Navigation", "DynamicBayesianNetwork"]\["TD", "H", "B", "MB"]\1\0\0\["Singapore"]\["National University of Singapore"]\["School of Computing"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "s", "k"]\\\\\["Grid"]
1563109146\Shaping in reinforcement learning via knowledge transferred from human-demonstrations\Some("https://ieeexplore.ieee.org/document/7260106/")\["t", "v", "a"]\["multi"]\["r", "rule"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "ClassicControl"]\["TD"]\1\1\1\["China"]\["Zhejiang University"]\["Aeronautics and Astronautic"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\[["FlappyBird", "Maze"]]\\\["Maze", "Grid"]
2896329788\Missile aerodynamic design using reinforcement learning and transfer learning\Some("http://engine.scichina.com/publisher/scp/journal/SCIS/61/11/10.1007/s11432-018-9463-x?slug=fulltext")\["t"]\["same_all"]\["fea"]\["j", "tr", "tt"]\\["Custom", "CS", "Formulas", "Figures"]\["DDPG", "SL-DDPG"]\[]\0\1\\["LiftDragRatio", "Simulation", "DDPG", "Aerodynamic", "Design", "Military", "Weapons"]\["TD"]\1\1\1\["China"]\["Tsinghua University"]\["Computer Science and Technology"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\
2792514232\Flexible Robotic Grasping with Sim-to-Real Transfer based Reinforcement Learning.\Some("http://hdl.handle.net/20.500.11850/322242")\["t"]\["pure", "sim2real"]\["pi"]\["j"]\\["Custom", "CS", "Formulas", "rllab", "PyBullet", "Figures", "Tables"]\["TRPO"]\[]\0\1\\["3D", "CNN",  "DepthCamera", "Robotics", "Grasping", "RealWorld", "Simulation"]\["TD"]\1\0\0\["Switzerland"]\["Eidgenössische Technische Hochschule Zürich"]\["Autonomous Systems Lab"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "s", "k"]\\[]\\\
2039109166\Rule abstraction and transfer in reinforcement learning by decision tree\Some("http://www.robot.t.u-tokyo.ac.jp/~yamashita/paper/B/B085Final.pdf")\["s_i", "s_f", "levels"]\["multi"]\["rule"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Obstacle", "CollisionAvoidance"]\["TD", "RRL"]\1\0\0\["Japan"]\["University of Tokyo"]\["Precision Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Obstacles"]
2550776844\Role of dopamine in learning beyond reinforcement: Boost of auditory learning and transfer by orally taken or gameplay-generated dopamine\Some("https://www.win.ox.ac.uk/publications/925528")\[]\["psychology"]\[]\[]\\[]\[]\[]\2\2\\["Psychology", "Tetris"]\[]\1\1\1\["China"]\["Beijing Normal University"]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2995352224\Single episode transfer for differing environmental dynamics in reinforcement learning\Some("https://openreview.net/pdf?id=rJeQoCNYDS")\["t"]\["same_all"]\["pi", "VAE"]\["tt", "tr"]\\["OSS", "TensorFlow", "Pseudo", "Formulas", "Figures"]\["DDQN", "SEPT"]\["exp"]\0\1\\["2D", "Simulation", "Navigation", "Grid", "Acrobot", "HIV", "SEPT", "ClassicControl"]\["TD"]\1\0\0\["USA"]\["Georgia Institute of Technology", "Lawrence Livermore National Laboratory"]\["Computer Science"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "s", "k"]\\\\\
2515676083\Transferring knowledge from human-demonstration trajectories to reinforcement learning\Some("http://journals.sagepub.com/doi/pdf/10.1177/0142331216649655")\["t"]\["same_all"]\["r", "KNN"]\["j", "ap", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["LSPI", "Q"]\["N/A"]\0\0\\["HumanDemonstration", "KNN", "Frequency", "CartPole", "Simulation", "2D", "ClassicControl"]\["TD", "batch"]\1\1\1\["China"]\["Zheijang University"]\["Aeronautics and Astronautics", "Control Science and Engineering"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
1981289969\Nash-reinforcement learning (N-RL) for developing coordination strategies in non-transferable utility games\Some("http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6974336")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["NashRL"]\[]\2\0\\["Games", "NTU", "Nash", "Simulation", "Hydropower", "Power", "Electricity", "ElectricityMarket", "MultiAgent"]\["TD"]\1\0\0\["UK", "USA", "Australia"]\["Imperial College London", "University of Central Florida", "The University of Melbourne"]\["Environmental Policy", "Environmental and Construction Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2085926075\Errorless Learning: Reinforcement Contingencies and Stimulus Control Transfer in Delayed Prompting.\Some("https://onlinelibrary.wiley.com/doi/10.1901/jaba.1984.17-175/references")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Neurosciences", "Neurology"]\[]\1\0\0\["USA"]\["Harvard Medical School", "California State College"]\["Neurology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
115717799\Transfer learning for reinforcement learning through goal and policy parametrization\Some("https://lirias.kuleuven.be/bitstream/123456789/131381/1/2006_wtl_driessens.pdf")\[]\["theory"]\[]\[]\\[]\[]\[]\2\2\\["Report", "State-of-the-Art"]\[]\1\0\0\["Belgium"]\["Katholieke Universiteit Leuven"]\["Computer Science"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "s", "k"]\\\\\
2803180393\Importance Weighted Transfer of Samples in Reinforcement Learning\Some("http://export.arxiv.org/abs/1805.10886")\["t", "s_f"]\["multi"]\["pi"]\["j", "tr", "tt"]\\["Custom", "CS",  "Formulas", "Pseudo", "Theorem", "Lemma", "Tables"]\["FQI", "RBT", "SDT", "IWFQI"]\["exp"]\1\0\\["Simulation", "Puddleworld", "WaterReservoirControl", "Acrobot", "ClassicControl"]\["TD", "MB", "batch"]\1\0\0\["Italy", "France"]\["Politecnico di Milano", "INRIA Lille"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2910593956\Design of Transfer Reinforcement Learning Mechanisms for Autonomous Collision Avoidance\Some("https://link.springer.com/chapter/10.1007/978-3-030-05363-5_17")\["s", "levels", "#"]\["multi"]\["advisor"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "TensorFlow", "Tables"]\["DQN"]\["N/A"]\0\1\\["2D", "Simulation", "Navigation", "Collision", "Avoidance"]\["TD"]\1\0\0\["USA"]\["University of Southern California"]\["Aerospace & Mechanical Engineering"]\3010746488\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\5\
3014664339\DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning\Some("https://www.sciencedirect.com/science/article/pii/S095741742030244X")\["s", "s_i", "s_f", "t"]\["multi", "lit"]\["PolicyLibrary"]\["tt"]\\["OSS", "TensorFlow", "Gym", "OpenCV", "Formulas", "Figures", "Pseudo"]\["Q", "DQN", "PLBI", "CBPI", "DECAF", "A2T"]\["N/A"]\0\1\\["Simulation", "2D", "Navigation", "Grid", "Games", "VideoGames", "Atari"]\["CBR", "TD"]\1\1\1\["Brazil"]\["University of Sao Paulo", "FEI’s University Centre"]\[]\0\["h", "lib"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2978426310\Strategy Selection in Complex Game Environments Based on Transfer Reinforcement Learning\Some("https://ieeexplore.ieee.org/document/8852019")\["a", "r", "t", "#"]\["lit"]\["advisor"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["A3C"]\["N/A"]\0\1\\["2D", "Games", "VideoGames", "Atari", "Simulation", "Imitation"]\["TD"]\1\1\1\["China", "Canada"]\["Dalian University of Technology", "McGill University"]\["Computer Science and Technology"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2810881727\Transfer with Model Features in Reinforcement Learning.\Some("https://arxiv.org/pdf/1807.01736")\["r", "t"]\["multi"]\["model", "fea"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Theorem", "Lemma"]\["Q", "Random"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "Simulation"]\["TD"]\1\0\0\["USA"]\["Brown University"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
929682\Speeding-up reinforcement learning through abstraction and transfer learning\Some("https://dl.acm.org/citation.cfm?id=2484942")\["s_i", "s_f"]\["multi"]\["pi"]\["tr", "ap"]\\["Custom", "CS",  "Formulas", "Pseudo", "Figures"]\["S2L-RL", "Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation", "Robotics"]\["TD", "RRL"]\1\0\0\["Brazil"]\["Universidade de São Paulo"]\[]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid", "Rooms", "SingleLevel"]
2604402446\Accelerating Multiagent Reinforcement Learning through Transfer Learning.\Some("https://dblp.uni-trier.de/db/conf/aaai/aaai2017.html#SilvaC17")\[]\["theory"]\[]\[]\\[]\[]\[]\2\2\\["Theory", "MultiAgent"]\[]\1\0\0\["Brazil"]\["Universidade de São Paulo"]\["Escola Politécnica"]\0\[]\["silva", "mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2812068818\Optimizing data transfers for improved performance on shared GPUs using reinforcement learning\Some("https://experts.syr.edu/en/publications/optimizing-data-transfers-for-improved-performance-on-shared-gpus")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Performance", "Optimization", "GPGPU", "Simulation", "MonteCarlo", "unrelated"]\[]\1\1\1\["USA"]\["Air Force Research Laboratory", "Syracuse University"]\["Information"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\\
2018629084\Towards reinforcement learning representation transfer\Some("https://dl.acm.org/citation.cfm?id=1329125.1329248")\["a", "v"]\["diff-it"]\["Q"]\["tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Web", "Dead"]\["SARSA"]\["sup"]\0\0\\["Simulation", "RoboCup", "KeepAway", "MultiAgent", "3v2"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2972017633\Transferring optimal contact skills to flexible manipulators by reinforcement learning\Some("https://dblp.uni-trier.de/db/journals/ijira/ijira3.html#XuPR19")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures", "Videos", "MIPS"]\["DDPG"]\[]\2\1\\["Robotics", "RealWorld", "Palpation", "Manipulators"]\["TD"]\1\1\1\["Singapore", "China"]\["National University of Singapore", "Tongji University"]\["Biomedical Engineering", "Control Science & Engineering"]\0\[]\["mag"]\["t", "r", "l", "s"]\["r", "l", "s"]\["t", "r", "l", "s", "k"]\\[]\\\
2240785302\Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem\Some("http://www.tandfonline.com/doi/full/10.1080/00207543.2015.1057297")\["t"]\["multi"]\["pi"]\["tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["Simulation", "Robotics", "FlowShop", "Collaboration", "Scheduling"]\["TD"]\1\0\0\["Israel"]\["Ben-Gurion University of the Negev"]\["Industrial Engineering and Management"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2969408081\Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet\Some("https://ui.adsabs.harvard.edu/abs/2019PSST...28i5019W/abstract")\["t"]\["pure", "sim2real"]\["pi"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Pseudo"]\["RLC", "G-RLC"]\[]\2\1\\["Robotics", "FeedbackControl", "AtmosphericPressurePlasma", "RealWorld"]\["TD"]\1\0\0\["USA", "Switzerland"]\["University of California", "Ecole Polytechnique Fédérale de Lausanne"]\["Chemical and Biomolecular Engineering", "Molecular Simulation"]\0\[]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l"]\\[]\\\
2344013593\Relational transfer in reinforcement learning\Some("https://minds.wisconsin.edu/handle/1793/60678")\["#", "t", "a", "v"]\["multi"]\["advisor", "rule", "Q", "MLN"]\["j", "tt", "ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Pseudo", "Web", "Dead"]\["Q", "MLN"]\["sup"]\0\0\\["Simulation", "RoboCup", "MultiAgent", "KeepAway", "BreakAway", "MoveDownfield", "2v1", "3v2", "4v3"]\["TD", "RRL", "B"]\1\0\0\["USA"]\["University of Wisconsin"]\["Computer Sciences"]\1\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2906148830\Relationship Between the Order for Motor Skill Transfer and Motion Complexity in Reinforcement Learning\Some("https://doi.org/10.1109/LRA.2018.2889026")\["s", "t"]\["sim2real", "curriculum", "multi"]\["pi"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q", "PoWER"]\["N/A"]\0\0\\["Robotics", "Simulation", "RealWorld", "MotorSkill", "Demonstration", "Expert", "Manipulation", "Drawing", "Fitting"]\["TD", "B", "MB"]\1\1\1\["South Korea"]\["Hanyang University", "Korea Institute of Industrial Technology"]\["Department of Electronics and Computer Engineering", "Smart Research Group"]\0\["h"]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\\\\\
2148179544\Structural knowledge transfer by spatial abstraction for reinforcement learning agents\Some("https://dblp.uni-trier.de/db/journals/adb/adb18.html#FrommbergerW10")\["t", "s_i", "s_f"]\["pure", "sim2real"]\["fea", "Q"]\["j", "tt", "tr", "ap"]\\["Custom", "CS",  "Formulas", "Pseudo", "Theorem", "Figures", "Tables", "Open-SLAM"]\["APSST", "Q"]\[]\0\0\\["Robotics", "Navigation", "RealWorld", "LRF", "Simulation"]\["TD"]\1\0\0\["Germany"]\["University of Bremen"]\["Cognitive Systems"]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "s", "k"]\["t", "r", "l", "s", "k"]\\\\\["Obstacles"]
2756467676\Transfer learning via linear multi-variable mapping under reinforcement learning framework\Some("http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8028754")\["#", "v"]\["multi"]\["Q"]\["tt"]\\["Custom", "CS",  "Formulas", "Figures", "Tables"]\["SARSA"]\["svg", "Ma"]\0\0\\["2D", "Simulation", "KeepAway", "Navigation", "MultiAgent", "RoboCup"]\["TD"]\1\0\0\["China"]\["National University of Defense Technology"]\["College of Mechatronics and Automation"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2093192040\Transfer in inverse reinforcement learning for multiple strategies\Some("http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6696817")\["s_i", "s_f", "levels"]\["multi"]\["advisor"]\["tt", "j"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Lemma"]\["StochasticPolicy"]\["N/A"]\0\0\\["Robotics", "Simulation", "MiniGolf", "Arm", "2D", "Navigation", "Grid"]\["TD"]\1\0\0\["Switzerland"]\["Ecole Polytechnique Federale de Lausann"]\["Learning Algorithms and Systems Laboratory (LASA)"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\["Grid"]
1912083604\Autonomous inter-task transfer in reinforcement learning domains\Some("http://www.dtic.mil/dtic/tr/fulltext/u2/1024624.pdf")\["v", "a", "#", "t", "s_i", "s_f", "s", "r"]\["same_all", "multi", "diff-no", "diff-it", "lit"]\["pi", "rule", "advisor", "Q"]\["j", "tt", "tr", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Pseudo", "KeepAway", "Web", "Dead"]\["Q", "SARSA", "NEAT", "FRM"]\["exp", "sup", "svg", "Ma"]\1\0\\["Simulation", "2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "ServerJob", "Scheduling", "KeepAway", "Ringworld", "KnightJoust", "MultiAgent", "ClassicControl", "RoboCup"]\["TD", "PS", "MB", "RRL"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\1\["h", "all", "lib", "mod"]\["mag", "bone"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
1481405077\Reinforcement learning transfer via common subspaces\Some("https://dblp.uni-trier.de/db/conf/atal/ala2011.html#Bou-AmmarT11")\["v", "a", "#", "t"]\["lit"]\["fea", "sub"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["FVI"]\["exp"]\1\0\\["2D", "CartPole", "Swingup", "MassSystem", "Simulation", "ClassicControl"]\["TD"]\1\1\1\["Netherlands", "USA"]\["Maastricht University", "Lafayette College"]\[]\0\["h"]\["mag", "zhu"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["SingleMass", "DoubleMass"], ["Pendulum", "CartpoleSwingup"]]\\\
2998488389\Compositional Transfer in Hierarchical Reinforcement Learning\Some("https://arxiv.org/abs/1906.11228")\["t", "#", "s_i", "s_f", "s"]\["multi", "sim2real"]\["off", "sub"]\["tt", "tr", "j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "TensorFlow", "Videos", "Web"]\["RHPO", "SAC"]\["N/A"]\0\1\\["3D", "Robotics", "Simulation", "RealWorld", "Lift", "Stack", "Pile"]\["H", "TD"]\1\0\0\["UK"]\["DeepMind"]\["Industry"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
3017464978\Sonic to knuckles: Evaluations on transfer reinforcement learning\Some("https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11425/114250J/Sonic-to-knuckles-Evaluations-on-transfer-reinforcement-learning/10.1117/12.2559546.full")\[]\[]\[]\[]\\["Custom", "CS"]\["PPO"]\[]\2\1\\["Control System", "Games", "VideoGames", "Sonic3", "Simulation", "Military"]\["TD"]\0\1\0\["USA"]\["Vanderbilt University", "Ohio University", "Air Force Research Lab"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\[]\\[]\\\
2150385772\Reinforcement learning transfer based on subgoal discovery and subtask similarity\Some("https://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7004683")\["s", "t", "levels"]\["multi"]\["sub", "options"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["Q", "SARSA", "SDHRL"]\["N/A"]\0\0\\["2D", "Navigation", "Maze", "Grid", "Simulation"]\["TD", "H"]\1\0\0\["China"]\["Nanjing University"]\["Computer Science and Technology"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\["Grid", "Maze"]
2887063207\Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning.\Some("https://www.preprints.org/manuscript/201808.0049/v1/download")\["t"]\["sim2real", "multi", "lit"]\["model"]\[]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "TensorFlow"]\["DDPG", "DRL-MTTP", "DQN"]\["N/A"]\0\1\\["3D", "Navigation", "Driving", "Trajectory", "Planning", "Autonomous", "Mobility", "Simulation", "RealWorld", "Vehicle"]\["TD"]\1\0\0\["China"]\["Central South University", "Harbin Institute of Technology", "Chongqing University", "Hunan University of Commerce"]\["Information Science and Engineering", "State Key Laboratory of Robotics and System", "State Key Laboratory of Mechanical Transmissions", "School of Computer and Information Engineering"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
1931215804\Transferring evolved reservoir features in reinforcement learning tasks\Some("https://rd.springer.com/chapter/10.1007%2F978-3-642-29946-9_22")\["v"]\["lit"]\["fea"]\["j", "tr", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "RL-Glue", "RL-Glue(deadlinks)"]\["NEAT"]\["exp"]\0\1\\["Simulation", "MountainCar", "2D", "3D", "2Dto3D", "Scheduling", "ServerJob", "ServerJobScheduling", "ClassicControl"]\["TD", "PS"]\1\0\0\["Greece"]\["Aristotle University of Thessaloniki", "Centre for Research and Technology Hellas"]\["Electrical & Computer Engineering", "Informatics and Telematics"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2604569119\Improving Deep Reinforcement Learning with Knowledge Transfer.\Some("https://dblp.uni-trier.de/db/conf/aaai/aaai2017.html#GlattC17a")\[]\["theory"]\[]\[]\\[]\["DQN"]\[]\2\2\\["Theory"]\["TD"]\1\0\0\["Brazil"]\["Universidade de São Paulo"]\["Escola Politécnica"]\0\[]\["mag", "multi"]\["t", "r", "l", "k"]\["t", "r", "l", "s"]\["t", "r", "l", "k", "s"]\\\\\
2808217720\Knowledge transfer in reinforcement learning\Some("https://www.didaktorika.gr/eadd/handle/10442/38511")\["s_i", "s_f", "t", "r"]\["same_all", "multi"]\["I", "fea"]\["j", "tr", "ap"]\\["Custom", "CS", "Pseudo", "Figures", "Tables", "Formulas"]\["Q", "MAXQ", "FQI", "LSPI", "LSTDQ"]\["N/A"]\0\0\\["MiniGolf", "MountainCar", "Boat", "2D", "Navigation", "Simulation", "ClassicControl"]\["batch", "H", "B", "TD"]\1\1\0\["Italy"]\["Politecnico di Milano"]\["Electronics and Information"]\1\["lib", "all"]\["taylor", "lazaric", "mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2100814505\The Perceived Impacts of Supervisor Reinforcement and Learning Objective Importance on Transfer of Training\Some("https://eric.ed.gov/?id=EJ597495")\[]\["unrelated"]\[]\[]\\[]\[]\[]\2\2\\["Finance", "Human", "Training", "unrelated"]\[]\1\1\1\["USA"]\["University of Minnesota", "Vital Consulting Group"]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2940691063\Human Causal Transfer: Challenges for Deep Reinforcement Learning.\Some("https://mindmodeling.org/cogsci2018/papers/0080/index.html")\[]\["psychology", "pure"]\[]\[]\\["Custom", "CS", "Gym", "Videos", "Formulas", "Figures"]\["DDQN"]\[]\0\1\\["2D", "Navigation", "Grid", "Simulation", "Human", "Causal", "Comparison"]\["TD"]\1\0\0\["USA"]\["UCLA", "Caltech", "UW"]\["Computer Science", "Psychology", "Jet Propulsion Laboratory", "Statistics"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2125946670\A framework for the adaptive transfer of robot skill knowledge using reinforcement learning agents\Some("https://dblp.uni-trier.de/db/conf/icra/icra2001.html#MalakK01")\["s_i", "s_f", "s", "levels"]\["multi"]\["advisor"]\["tt"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Robotics", "Simulation"]\["TD"]\1\0\0\["USA"]\["Carnegie Mellon University"]\["Electrical and Computer Engineering"]\0\["lib"]\["mag"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\\\\\["Grid", "Maze"]
2967621954\UAV Trajectory Design Based on Reinforcement Learning for Wireless Power Transfer\Some("https://ieeexplore.ieee.org/document/8793294")\[]\["pure"]\[]\[]\\["Custom", "CS",  "Formulas", "Figures"]\["Q"]\[]\2\0\\["Wireless Power Transfer", "SmartEnergy", "PureRL", "Drones", "UAV", "Vehicle", "Aerial", "Simulation"]\[]\1\1\1\["South Korea"]\["Yonsei University"]\["Electrical and Electronics Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\\
2398715346\Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning.\Some("https://experts.umn.edu/en/publications/advice-taking-and-transfer-learning-naturally-inspired-extensions")\["#"]\["multi"]\["rule", "advisor"]\["j"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "RoboCup", "BreakAway", "MultiAgent", "Simulation", "3v2", "2v1"]\["TD", "RRL"]\1\0\0\["USA"]\["University of Wisconsin", "University of Minnesota"]\["Computer Sciences"]\0\[]\["mag"]\["t", "r", "l"]\["t", "l"]\["t", "r", "l", "k", "s"]\\[["BreakAway2v1", "BreakAway3v2"]]\\\
1573527757\Transfer of task representation in reinforcement learning using policy-based proto-value functions\Some("http://doi.acm.org/10.1145/1402821.1402864")\["s_f", "t"]\["multi"]\["pvf"]\["j", "tr", "ap", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Grid", "ThreeRoomMaze"]\["TD"]\1\0\0\["Italy"]\["Politecnico di Milano"]\[]\0\[]\["mag", "lazaric"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\["ThreeRoomMaze", "Rooms"]
1255659923\Fixed vs. Dynamic Sub-Transfer in Reinforcement Learning.\Some("https://dblp.uni-trier.de/db/conf/icmla/icmla2002.html#CarrollP02")\["s_i", "s_f", "s", "levels"]\["multi"]\["pi_fix", "pi", "pi_dyn"]\["tr", "ap", "j"]\\["Custom", "CS", "Formulas",  "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Maze", "Grid", "ThreeRoomMaze"]\["TD"]\1\0\0\["USA"]\["Brigham Young University"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "l"]\["t", "r", "l", "s"]\\\\\["Grid", "ThreeRoomMaze", "Rooms"]
2945961113\Effects of Task Similarity on Policy Transfer with Selective Exploration in Reinforcement Learning\Some("https://dl.acm.org/citation.cfm?id=3332034")\["t", "s_i", "s_f", "s", "levels"]\["multi"]\["model"]\["j", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Taxi", "Grid"]\["TD", "MB"]\1\0\0\["Singapore"]\["National Unversity of Singapore"]\["Computing"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2885067833\Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement Learning.\Some("https://dblp.uni-trier.de/db/conf/aaai/aaai2018w.html#Winderd18")\[]\["unavailable"]\["fea"]\[]\\[]\[]\["exp"]\0\2\\["ConceptAware", "Features", "FeatureExtraction"]\[]\0\0\0\[]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2991117878\Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning.\Some("https://arxiv.org/pdf/1911.12851.pdf")\["v"]\["same_all"]\["model"]\["tr"]\\["Custom", "CS",  "Formulas", "PyTorch", "Gym", "Figures", "Tables"]\["DQN", "AVAE"]\[]\1\1\\["Input Abstraction", "VideoToSound", "Atari", "Simulation", "VideoGames", "Games"]\["TD"]\1\0\0\["Portugal", "USA"]\["INESC-ID", "Instituto Superior Técnico", "Carnegie Mellon University"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l"]\\[["AtariImage", "AtariSound"]]\\\
2942204155\Transferring Task Goals via Hierarchical Reinforcement Learning\Some("https://openreview.net/pdf?id=S1Y6TtJvG")\["t", "a"]\["multi"]\["r", "Hierarchical"]\["j", "tr", "ap", "tt"]\\["Custom", "CS", "Formulas", "Mujoco", "Figures", "Videos"]\["HRL"]\["exp"]\1\1\\["2D", "3D", "Robotics", "Navigation", "Simulation", "Ball", "Ant"]\["TD", "H"]\1\0\0\["USA", "UK"]\["University of California", "DeepMind"]\["Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l"]\[]\["t", "r", "l", "k"]\\\1\\
1990654808\Context transfer in reinforcement learning using action-value functions\Some("https://core.ac.uk/display/88336304")\["v", "a"]\["diff-no"]\["fea", "Q"]\["j", "tt", "ap", "tr"]\\["Custom", "CS", "Formulas", "Theorem", "Figures"]\["Q"]\["sup"]\0\0\\["Simulation", "2D", "Navigation", "Grid", "Collect", "Robots", "CrossroadTrafficController"]\["TD"]\1\0\0\["Iran"]\["University of Tehran", "Institute for Research in Fundamental Sciences"]\["Cognitive Robotics Lab", "College of Engineering", "Electrical and Computer Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid", "Obstacles"]
112971866\Transfer in Reinforcement Learning Domains\Some("https://doi.org/10.1007/978-3-642-01882-4")\[]\["theory", "book"]\[]\[]\\[]\[]\[]\2\2\\["Theory"]\[]\1\1\1\["USA"]\["University of Southern California"]\["Computer Science"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\[]\\\\\
2910911697\Boosting Reinforcement Learning in Competitive Influence Maximization with Transfer Learning\Some("https://dblp.uni-trier.de/db/conf/webi/webi2018.html#AliWC18")\["s", "s_i", "s_f"]\["same_all"]\["pi", "Q"]\["tt"]\\["Custom", "CS", "graph-tool", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["Simulation", "SocialNetworks", "Social", "Graph", "Influence", "Maximization"]\["TD"]\1\1\1\["Taiwan"]\["Academia Sinica", "National Tsing Hua University"]\["Social Networks and Human-Centered Computing", "Information Science", "Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2990442018\Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry\Some("https://pubag.nal.usda.gov/catalog/6767089")\[]\["pure"]\[]\[]\\["Custom", "CS",  "Formulas", "Figures", "Tables"]\[]\[]\2\0\\["Wireless Power Transfer", "SmartEnergy", "PureRL", "Drones", "UAV", "Vehicle", "Aerial", "Simulation"]\[]\1\0\0\["Italy"]\["University of Catania"]\["Electronics and Information"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[]\\\
3008535267\"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer.\Some("http://export.arxiv.org/abs/1909.11730")\["t", "s", "s_i", "s_f"]\["sim2real", "multi"]\["r", "ExperienceReplay"]\["tr", "ap"]\\["OSS", "Custom", "PyTorch", "Gym", "Figures", "Formulas", "Pseudo", "Tables", "Videos"]\["VPG", "DQN"]\["N/A"]\0\1\\["3D", "VisualGrasping", "Robotics", "Simulation", "RealWorld", "Arm", "2D", "Navigation", "Grid"]\["TD"]\1\0\0\["USA"]\["The Johns Hpkins University", "NVIDIA"]\["Industry", "Uni", "Colab"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2982398001\A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning\Some("https://www.worldscientific.com/doi/10.1142/S0218001420520059")\["s"]\["same_all"]\["fea"]\["ap"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["DQN"]\["N/A"]\0\1\\["SmartEnergy", "Power", "Consumption", "Prediction", "Simulation"]\["TD"]\1\1\1\["China", "Canada"]\["Suzhou University of Science and Technology", "McMaster University"]\["Electronics and Infromation Engineering", "Key Laboratory of Intelligent Building Energy Efficiency", "Engineering"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2965163470\Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns\Some("https://www.ijcai.org/Proceedings/2019/65")\["t", "s_i", "s_f", "s", "levels"]\["multi"]\["Q"]\["j", "tr", "tt", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["Q", "DQN"]\["N/A"]\0\1\\["2D", "Navigation", "Maze", "MARL", "Atari", "Games", "Simulation", "MultiAgent", "Pacman", "PredatorPrey"]\["TD"]\1\0\0\["China"]\["Nanjing University", "Netease"]\["National Key Laboratory for Novel Software Technology", "Fuxi AI Lab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3006698967\A Dynamic Financial Knowledge Graph Based on Reinforcement Learning and Transfer Learning\Some("https://doi.org/10.1109/BigData47090.2019.9005691")\[]\["same_all"]\["KnowledgeGraph"]\["tr"]\\["Custom", "CS", "Figures", "Tables"]\["BERT"]\[]\0\1\\["Finance", "Simulation", "KnowledgeGraph", "CNN", "NLP"]\["TD"]\1\1\1\["China"]\["Peking University", "Beijing Normal University"]\["EECS", "Government"]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2971274224\A Study on Efficient Transfer Learning for Reinforcement Learning Using Sparse Coding\Some("http://www.joace.org/uploadfile/2015/1023/20151023022908338.pdf")\["s_i", "s_f", "s", "levels"]\["multi"]\["sparse_coding"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Maze", "Simulation"]\["TD"]\1\0\0\["Japan"]\["Ochanomizu University"]\["School of Humanities and Sciences"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2900640207\ASD: A Framework for Generation of Task Hierarchies for Transfer in Reinforcement Learning\Some("https://dblp.uni-trier.de/db/conf/iconip/iconip2018-3.html#GoyalMNR18")\[]\["theory"]\[]\[]\\["Custom", "CS", "Pseudo", "Figures"]\["HRL"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Taxi", "Wargus", "Collect", "MultiAgent", "Simulation"]\["TD", "H"]\1\0\0\["India", "Singapore"]\["IIIT Bangalore", "National University of Singapore"]\["International Institute of Information Technology"]\0\[]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
2986543185\Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation\Some("https://doi.org/10.3390/info10110341")\["#", "s_i", "s_f", "s"]\["same_all"]\["curriculum"]\["j", "tt"]\\["Custom", "CS", "Robocode", "Formulas", "Figures", "Pseudo", "Tables"]\["SSAQ", "Q"]\["N/A"]\0\1\\["2D", "Robotics", "RoboCode", "Simulation", "SharedParameters", "MultiAgent"]\["TD", "MB"]\1\0\0\["China"]\["Hubei University of Arts and Science", "Northwestern Polytechnical University"]\["Computer Engineering", "Computer Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2964998356\Skill based transfer learning with domain adaptation for continuous reinforcement learning domains\Some("https://dblp.uni-trier.de/db/journals/apin/apin50.html#ShoelehA20")\["s_i", "s_f", "s", "levels"]\["multi"]\["Q", "graph"]\["tt", "ap", "tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["GSL", "SARSA", "TLDA"]\["N/A"]\0\0\\["Simulation", "2D", "Pinball", "GraphBased", "MountainCar", "3D", "MountainCar3D", "MountainCar2Dto3D", "ClassicControl"]\["TD", "H"]\1\1\1\["Iran"]\["University of Tehran"]\["Electrical and Computer Engineering"]\0\["all"]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\["t", "r", "l", "s", "k"]\\\\\
2976782275\MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics\Some("https://arxiv.org/pdf/1909.13111")\["v", "a", "r", "t", "s", "s_i", "s_f"]\["diff-no", "lit"]\["pi", "pi_gen", "distil"]\["j", "tr"]\\["Custom", "CS", "bootstrapped", "Gym", "stable-baselines", "Figures", "Formulas", "Tables"]\["MLP", "RPL", "MULTIPOLAR"]\["N/A"]\1\1\\["2D", "Robotics", "Simulation", "Gym", "CartPole", "Acrobot", "LunarLanderContinuous", "Mujoco", "Hopper", "Ant", "InvertedPendulum", "ClassicControl"]\["TD"]\1\0\0\["Germany", "Japan"]\["Technical University of Munich", "OMRON SINIC X"]\["Industry", "Uni", "Colab"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2562252866\Effective Transfer via Demonstrations in Reinforcement Learning: A Preliminary Study\Some("https://dblp.uni-trier.de/db/conf/aaaiss/aaaiss2016.html#WangT16")\[]\["multi"]\["rule"]\["j", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Simulation", "Navigation", "KeepAway", "MultiAgent", "RoboCup"]\["TD"]\1\0\0\["USA"]\["Washington State University"]\["EECS"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "l", "k"]\["t", "r", "l", "k"]\\\\\
2977949361\Manufacturing Dispatching using Reinforcement and Transfer Learning.\Some("http://arxiv.org/pdf/1910.02035.pdf")\["#", "t", "s", "s_i"]\["same_all"]\["pi", "manifold"]\["j", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["DMD"]\["N/A"]\0\1\\["Scheduling", "Dispatching", "Simulation"]\["TD"]\1\0\0\["USA"]\["Hitachi Industrial AI Lab"]\["Industry"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\\
2524747780\Learning a transfer function for reinforcement learning problems\Some("https://lirias.kuleuven.be/bitstream/123456789/203706/1/Croonenborghs.pdf")\["s", "levels"]\["same_all"]\["Q"]\["ap", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q", "SARSA", "TILDE"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation"]\["TD"]\1\0\0\["Belgium"]\["KH Kempen University College", "Katholiege Universiteit Leuven"]\["Biosciences and Technology", "Declarative Languages and Artifical Intelligence"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2070732802\The role of temporal statistics in the transfer of experience in context-dependent reinforcement learning\Some("https://ieeexplore.ieee.org/document/7086184/")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["ANN", "HIS", "TemporalCredit", "Simulation"]\[]\1\1\1\["Kuwait"]\["Arab Open University"]\["Computer Studies"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
1569756368\Reinforcement learning transfer using a sparse coded inter-task mapping\Some("https://core.ac.uk/display/158764893")\["t", "a", "v"]\["diff-no", "lit"]\["Q", "I"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["LSPI", "FQI"]\["exp"]\0\0\\["2D", "Simulation", "MountainCar", "InvertedPendulum", "CartPole", "ClassicControl"]\["TD", "batch"]\1\0\0\["Netherlands", "USA"]\["Maastricht University", "Lafayette College"]\[]\126751897\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\1\
2900471431\Feature Learning and Transfer Performance Prediction for Video Reinforcement Learning Tasks via a Siamese Convolutional Neural Network\Some("https://link.springer.com/chapter/10.1007/978-3-030-04167-0_32")\["#", "s_i", "s_f", "s", "levels"]\["multi"]\["Q", "fea"]\["j", "tt"]\\["Custom", "CS", "Figures", "Pseudo", "Formulas", "Tables"]\["SARSA", "Q"]\["exp"]\0\1\\["2D", "Navigation", "Games", "VideoGames", "CNN", "Siamese", "Atari", "PacMan", "Maze", "Simulation"]\["TD"]\1\1\1\["China"]\["Nanjing University"]\["Key Laboratory for Novel Software Technology", "Collaborative Innofation of Novel Software Technology and Industrialization"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2980820015\Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer.\Some("https://www.arxiv-vanity.com/papers/1910.09471/")\["t"]\["multi", "sim2real"]\["pi"]\["tr", "tt", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Mujoco", "SciPy", "Tables", "Videos"]\["MPO"]\["N/A"]\0\1\\["Robotics", "Simulation", "RealWorld", "Arm", "Grab"]\["TD"]\1\0\0\["UK"]\["DeepMind"]\["Industry"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\2\\
2021066679\Transfer Method for Reinforcement Learning in Same Transition Model -- Quick Approach and Preferential Exploration\Some("https://dblp.uni-trier.de/db/conf/icmla/icmla2011-1.html#TakanoTKT11")\["s_i", "s_f", "s", "levels"]\["multi"]\["rule", "pi"]\["tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD"]\1\1\1\["Japan"]\["Mie University"]\["Engineering", "Regional Innovation Studies"]\2062960870\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\3\
2967582614\Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity.\Some("https://arxiv.org/pdf/1908.05265v2")\["v", "a"]\["lit"]\["VAE", "latent"]\["j", "tr", "ap", "tt"]\\["Custom", "CS", "Theorem", "Figures", "Formulas", "Gym", "Tables"]\["PPO", "PVED"]\["N/A"]\0\1\\["2D", "Robotics", "Mujoco", "Hopper", "Bipedal", "Walker2d", "Jaco3", "Simulation"]\["TD"]\1\0\0\["UK"]\["University of Warwick"]\["Warwick Manufacturing Group"]\0\["h"]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\["t", "r", "l", "s", "k"]\\\2\\
2123178778\Transfer of knowledge for a climbing Virtual Human: A reinforcement learning approach\Some("https://ieeexplore.ieee.org/document/5152553/")\["s_i", "s_f"]\["same_all"]\["Q", "fea"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Tables", "Figures"]\["Dyna-Q"]\["N/A"]\0\0\\["Simulation", "Robotics", "Climbing", "Human", "Joints"]\["TD"]\1\0\0\["France"]\["Université Paris 6", "Laboratoire d’Intégration des Systèmes et des Technologies in Commissariat à l’Énergie Atomique"]\["Intelligent Systems and Robotics", "Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
3000319261\A Reinforcement Learning Approach for Efficient Opportunistic Vehicle-to-Cloud Data Transfer.\Some("http://arxiv.org/pdf/2001.05321.pdf")\[]\["pure"]\[]\[]\\[]\[]\[]\2\2\\["Vehicle", "Cloud"]\[]\1\0\0\["Germany"]\["TU Dortmund"]\["Communication Networks Institute"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\2\\
2056107474\Efficient Knowledge Transfer in Shaping Reinforcement Learning\Some("http://www.sciencedirect.com/science/article/pii/S1474667016450536")\["t", "r", "s", "a"]\["multi"]\["Q"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation", "Obstacle"]\["TD"]\1\0\0\["Netherlands"]\["Delft University of Technology"]\["Center for Systems and Control"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\["Grid", "Obstacles"]
2751021526\Towards Knowledge Transfer in Deep Reinforcement Learning\Some("http://jglobal.jst.go.jp/en/public/20090422/201702272924443642")\["s", "levels", "t"]\["diff-no"]\["pi"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["DQN"]\["N/A"]\0\1\\["2D", "Games", "VideoGames", "Simulation", "Atari", "Breakout", "Atlantis", "Boxing"]\["TD"]\1\0\0\["Brazil"]\["Escola Politécnica da Universidade de Sao Paulo"]\[]\0\["h"]\["mag", "silva"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\[["Atari", "Atari"]]\\\
3010496493\Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning\Some("https://vbn.aau.dk/da/publications/transferring-human-manipulation-knowledge-to-robots-with-inverse-")\["t"]\["same_all"]\["I"]\[]\\["Custom", "CS", "Figures", "Formulas"]\["Q"]\[]\0\0\\["Robotics", "Simulation", "RealWorld", "Imitation", "UniversalRobot"]\["TD"]\1\0\0\["Denmark"]\["Aalborg University"]\["Robotics and Automation"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2945719686\Improving Deep Reinforcement Learning via Transfer\Some("https://dl.acm.org/citation.cfm?id=3332128")\[]\["theory"]\[]\[]\\[]\[]\[]\2\2\\["CNN"]\[]\1\0\0\["USA"]\["Washington State University"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2040298429\Serial pattern learning by rats: Transfer of a formally defined stimulus relationship and the significance of nonreinforcement\Some("https://link.springer.com/article/10.3758%2FBF03209273")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\1\0\0\["USA"]\["The Johns Hopkins University"]\["Psychology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
3024199149\Efficient Deep Reinforcement Learning via Adaptive Policy Transfer\Some("http://arxiv.org/pdf/2002.08037.pdf")\["s_i", "s_f", "s", "levels", "t"]\["multi"]\["pi", "pi_dyn", "options"]\["j", "tr", "tt", "ap"]\\["Custom", "CS", "fakeOSS", "Figures", "Formulas", "Pseudo"]\["PTF-A3C", "PTF-PPO", "A3C", "PPO"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Simulation", "Games", "Pinball", "Reacher", "Robotics"]\["TD", "H"]\1\0\0\["China"]\["Tianjin University", "Huawei", "Nanjing University", "Netease", "JD Digits"]\["Intelligence and Computing", "Noahs Ark Lab", "Machine learning"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2910862680\Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning\Some("https://openreview.net/pdf?id=H1gZV30qKQ")\["t"]\["same_all"]\["pi"]\["j", "tt", "ap"]\\["Custom", "CS", "Gym", "Mujoco", "baselines", "PyTorch", "Figures", "Formulas", "Pseudo", "Theorem"]\["MVC", "TRPO", "DDPG"]\["N/A"]\0\1\\["2D", "3D", "Robotics", "Model", "ValueCentric", "Mujoco", "HalfCheetah", "InvertedPendulum", "Pendulum", "Reacher"]\["TD", "MB"]\1\0\0\["USA"]\["University of California"]\[]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\1\\
2945090640\Options in Multi-task Reinforcement Learning - Transfer via Reflection\Some("https://dblp.uni-trier.de/db/conf/ai/ai2019.html#DenisF19")\["s_i", "s_f"]\["same_all"]\["options"]\["j", "tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["DQN", "LOVR"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Lifelong", "Landmark", "CNN", "Simulation"]\["TD", "H"]\0\1\1\["Canada"]\["University of Ottawa"]\["Mathematics and Statistics"]\0\["h"]\["mag", "multi"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2954881914\On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning.\Some("https://export.arxiv.org/pdf/1907.00884")\["s_f"]\["same_all"]\["r", "options"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["DQN", "LOVR", "Zombie"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Cliff-Walk", "Lifelong", "Landmark", "Simulation"]\["TD", "H"]\1\0\0\["Canada"]\["University of Ottawa"]\["Mathematics and Statistics"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\2\\
2443629117\A Verification of Reinforcement Learning with Knowledge Transfer and Knowledge Selection in Multitask Learning\Some("https://www.jstage.jst.go.jp/article/iscie/29/3/29_152/_article/-char/ja/")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["DifferentLanguage"]\[]\1\0\0\["Japan"]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2994229809\Dynamic pricing of demand response based on elasticity transfer and reinforcement learning\Some("http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8921683")\["t"]\["same_all"]\["Q"]\["j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["SARSA"]\["N/A"]\0\0\\["Power", "ElectricityMarket", "Electricity", "Simulation", "DynamicPricing", "Dynamic", "Demand"]\["TD"]\1\1\1\["China"]\["Tianjin University", "State Grid Tianjin Electric Power", "Beijing Fibrlink Corperation Company"]\["Key Laboratory of Smart Grid", "Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2963248502\Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning.\Some("https://arxiv.org/abs/1907.10580")\["s_i", "s_f", "s"]\["same_all", "multi"]\["options", "sub"]\["j", "tr"]\\["fakeOSS", "Custom", "PyTorch", "A2C-Kostrikov", "Gym", "Tables", "Figures", "Formulas", "Pseudo"]\["A2C", "IR-VIC"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Maze", "Room", "MultiRoom", "Simulation", "CNN"]\["TD"]\1\0\0\["USA"]\["Georgia Institute of Technology", "Facebook AI Research"]\["Industry", "Uni", "Colab"]\0\[]\["mag"]\["t", "r", "l"]\[]\["t", "r", "l", "k"]\\\\\
2979305570\A Weight Transfer Mechanism for Kernel Reinforcement Learning Decoding in Brain-Machine Interfaces\Some("https://www.ncbi.nlm.nih.gov/pubmed/31946644")\["a"]\["same_all"]\["WTF"]\["j"]\\["Custom", "CS", "Formulas", "Figures"]\["Softmax"]\["N/A"]\0\0\\["BMI", "KernelRL", "Mice", "Simulation"]\["TD"]\1\1\1\["China"]\["Hong Kong University of Science and Technology"]\["Electronic and Computer Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2786530308\Shaping in reinforcement learning by knowledge transferred from human-demonstrations of a simple similar task\Some("https://dblp.uni-trier.de/db/journals/jifs/jifs34.html#WangFL18")\["t", "v", "a"]\["diff-it"]\["r"]\["tt", "j", "ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["sup"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar3D", "Simulation", "MountainCar", "Human", "Demonstration", "ClassicControl"]\["TD"]\1\1\1\["China"]\["Zhejiang University"]\["Aeronatuics and Astronautics", "Schoolf of Control Science and Engineering"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2399136500\Reducing Sample Complexity in Reinforcement Learning by Transferring Transition and Reward Probabilities\Some("http://dx.doi.org/10.5220/0004915606320638")\["s", "levels"]\["same_all", "multi"]\["r", "model", "transitiondynamics"]\["j", "tr", "tt", "ap"]\\["Custom", "CS",  "Formulas", "Pseudo", "Theorem", "Tables", "Figures"]\["TR-MAX", "R-MAX", "Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Room", "MultiRoom", "Simulation"]\["TD", "MB"]\1\0\0\["Japan"]\["Tohoku University"]\["Information Sciences"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3023198000\GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning.\Some("http://arxiv.org/pdf/2005.00406.pdf")\[]\["pure"]\[]\[]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\[]\[]\2\1\\["Circuit Design", "Transistor Sizing"]\["TD", "B"]\1\0\0\["USA"]\["Massachusetts Institute of Technology", "The University of Texas"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2889943195\Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning\Some("https://nips.cc/Conferences/2018/Schedule?showEvent=11239")\["#", "s", "levels", "s_i", "s_f"]\["multi"]\["transitiondynamics"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Theorem", "Figures"]\["DOORMAX", "OO-MDP"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Taxi", "Sokoban"]\["TD", "RRL", "MB"]\1\0\0\["South Africa"]\["University of the Witwaterstrand", "Council for Scientific and Industrial Research"]\["Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
1974126445\Implementation of Reinforcement Learning by transfering sub-goal policies in robot navigation\Some("https://dblp.uni-trier.de/db/conf/siu/siu2013.html#GokceA13")\["s_i", "s_f", "s"]\["same_all"]\["pi"]\["tr"]\\["Custom", "CS", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Robotics", "Rooms", "Simulation"]\["TD"]\1\0\0\["Turkey"]\["Bogazici University"]\[]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\[]\\\\\["Grid", "Rooms"]
1997664188\Transfer Learning Method Using Ontology for Heterogeneous Multi-agent Reinforcement Learning\Some("https://thesai.org/Publications/ViewPaper?Volume=5&Issue=10&Code=IJACSA&SerialNo=22")\["a", "v", "s_i", "s_f", "s"]\["diff-no", "lit"]\["Q", "federated"]\["j", "tr", "tt", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "PredatorPrey", "MultiAgent", "Simulation"]\["TD", "H"]\1\0\0\["Japan"]\["Tokyo Denki University", "National Institute of Advanced Industrial Science and Technology (AIST)"]\["Advanced Science and Technology", "Information and Communication Engineering", "Intelligent Systems Research Institute"]\0\["h"]\["mag", "silva"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid", "PredatorPrey"]
2972522277\Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning\Some("https://export.arxiv.org/abs/1909.04307")\["s_i", "s_f", "s", "levels"]\["diff-no"]\["pri"]\["tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Gym", "Figures", "Theorem"]\["Q", "A2C", "SARSA", "DQN"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Continuous", "Maze", "Simulation", "SafetyGym"]\["TD"]\1\0\0\["Australia"]\["Deakin University"]\["Applied Artificial Intelligence"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
2323475154\A Hybrid Transfer Algorithm for Reinforcement Learning Based on Spectral Method\Some("http://pub.chinasciencejournal.com/article/getArticleRedirect.action?doiCode=10.3724/SP.J.1004.2012.01765")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["2D", "Simulation", "Navigation", "Grid", "Spectral", "Simulation"]\[]\0\1\0\["China"]\["University of Mining and Technology"]\["Information and Electrical Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\\\\
2991344719\Attention Privileged Reinforcement Learning for Domain Transfer\Some("https://export.arxiv.org/pdf/1911.08363")\["#", "t", "s", "s_i", "s_f"]\["same_all"]\["pi"]\["tt", "ap", "tr"]\\["Custom", "CS", "TensorFlow", "Formulas", "Pseudo", "Figures", "Web", "Videos"]\["aDDPG", "APRiL"]\["N/A"]\0\1\\["Simulation", "Robotics", "2D", "Navigation", "Arm", "Legs", "Walker2d", "JacoReach", "Simulation"]\["TD"]\1\0\0\["UK"]\["University of Oxford", "DeepMind"]\["Applied AI Lab", "Industry", "Uni", "Colab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2372578164\Transfer of Reinforcement Learning:The State of the Art\Some("http://en.cnki.com.cn/Article_en/CJFDTOTAL-DZXU2008S1006.htm")\[]\["theory"]\[]\[]\\[]\[]\[]\2\2\\["Theory"]\[]\0\1\0\["China"]\["Nanjing University", "State Key Laboratory of Novel Software Technology"]\[]\0\[]\["mag"]\["t", "r" ,"l"]\["t", "r" ,"l", "k"]\[]\\\\\
1178202497\A model-based markovian context-dependent reinforcement learning approach for neurobiologically plausible transfer of experience\Some("https://dblp.uni-trier.de/db/journals/ijhis/ijhis12.html#Hamid15")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["ANN", "HIS", "TemporalCredit", "Simulation"]\[]\1\1\1\["Kuwait"]\["Arab Open University"]\["Computer Studies"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
143164768\Using Options for Knowledge Transfer in Reinforcement Learning TITLE2\Some("https://dl.acm.org/citation.cfm?id=897568")\["t"]\["multi"]\["options"]\["tt"]\\["Custom", "CS", "Figures", "Formulas"]\["Q" ]\["N/A"]\0\0\\["MountainCar", "Simulation", "ClassicControl"]\["TD"]\1\0\0\["USA"]\["University of Massachusetts"]\[]\1510402218\["all"]\["taylor", "mag", "lazaric"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\[]\\3\
2611612482\Bacteria Foraging Reinforcement Learning for Risk-Based Economic Dispatch via Knowledge Transfer\Some("https://ideas.repec.org/a/gam/jeners/v10y2017i5p638-d97735.html")\["s"]\["same_all"]\["Q"]\["j"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["BFRL"]\[]\0\0\\["Power", "Efficiency", "SmartEnergy", "Effectiveness", "Simulation", "IEEE RTS-19", "Bacteria", "MultiAgent"]\["TD"]\1\0\0\["China"]\["South China University of Technology", "Kunming University of Science and Technology"]\["Electric Power Engineering"]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3013348603\Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning.\Some("http://arxiv.org/pdf/2003.13085.pdf")\["s_i", "s_f"]\["multi"]\["advisor"]\["tr", "tt", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["RL", "DQN", "DDPG"]\["N/A"]\0\1\\["2D", "Navigation", "Simulation", "Grid", "Collect", "Cooperation", "MultiAgent"]\["TD"]\1\0\0\["USA", "China"]\["Carnegie Mellon University", "Sun Yat-sen University"]\["Robotics", "Mathematics"]\0\[]\["mag", "multi"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
1696410204\Experiments with Adaptive Transfer Rate in Reinforcement Learning\Some("https://rd.springer.com/chapter/10.1007/978-3-642-01715-5_1")\["s_i", "s_f"]\["same_all"]\["pi"]\["j", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["Q", "PPR", "MGE"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Grid"]\["TD", "H"]\1\1\1\["France"]\["Université Paris-Dauphine", "Université Paris 6", "Centre IRD de l Ile de France"]\[]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid"]
2896823681\Episodic memory transfer for multi-task reinforcement learning\Some("https://www.sciencedirect.com/science/article/abs/pii/S2212683X18300902")\["v"]\["multi"]\["LSTM"]\["ap"]\\["Custom", "CS", "Formulas"]\["SEM-PAAC", "PAAC", "A3C-LSTM"]\["N/A"]\0\1\\["2D", "Navigation", "Taxi", "Simulation", "Multitask", "Memory", "EpisodicMemory"]\["TD", "H"]\1\1\1\["Russia"]\[]\[]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "s"]\["t", "r", "l", "k", "s"]\\\\\
2984781810\Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation\Some("https://export.arxiv.org/pdf/1911.07450")\["s_i", "s_f", "s", "levels"]\["multi"]\["Curriculum"]\["tt", "tr", "ap"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["A3C", "LSTM"]\["N/A"]\0\1\\["3D", "Navigation", "Simulation", "Hypernetwork", "AI2-THOR"]\["TD", "H", "MB"]\1\0\0\["China", "USA"]\["Zheijang University", "University of California"]\[]\0\[]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\\\2\\
3027086341\Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text\Some("http://arxiv.org/pdf/2005.09382.pdf")\[]\["same_all"]\["WT"]\["ap"]\\["Custom", "CS", "BERT", "TensorFlow", "Figures", "Tables"]\["BERT"]\["N/A"]\0\1\\["NLP", "TextInstructions", "3D", "Unity", "Simulation"]\["TD"]\1\0\0\["USA"]\["DeepMind"]\["Industry"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\2\\
2962744619\An improved reinforcement learning algorithm based on knowledge transfer and applications in autonomous vehicles\Some("https://dblp.uni-trier.de/db/journals/ijon/ijon361.html#DingDWH19")\["v", "t"]\["lit"]\["cases", "Q", "pi_gen"]\["j"]\\["Custom", "CS",  "Formulas", "Pseudo", "Figures", "Tables"]\["Q", "KT-HA-Q"]\["sup"]\0\0\\["2D", "ClassicControl", "Simulation", "2Dto3D", "3D", "MountainCar", "MountainCar3D"]\["TD", "CBR"]\1\1\1\["China"]\["University of Shanghai", "Northeast Petroleum University"]\["Control Science and Engineering", "Complex Systems and Advanced Control"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\\\\\
2901461022\Self-organizing maps for storage and transfer of knowledge in reinforcement learning:\Some("https://doi.org/10.1177/1059712318818568")\["s_i", "s_f"]\["same_all"]\["Q"]\["tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["SOM", "Q"]\["SOM"]\0\0\\["SOF", "SelfOrganizingMap", "2D", "Navigation", "Simulation"]\["TD"]\1\0\0\["Singapore"]\["Singapore University of Technology and Design"]\["Computer Science"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3003281790\Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning.\Some("http://downloads.hindawi.com/journals/cin/2020/8989752.pdf")\["s"]\["lit"]\["pi"]\["ap"]\\["Custom", "CS", "Formulas", "Pseudo", "TensorFlow", "Tables"]\["Q"]\["exp"]\0\1\\["Simulation", "LightFieldReconstruction", "PCA", "Q", "Image", "ImageRecognition", "CNN", "Vehicle", "MultiAgent"]\["TD"]\1\0\0\["China"]\["Henan Institue of Science and Technology", "Shandong University"]\["Artifical Intelligence", "Information Engineering", "Control Science and Engineering"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2965999293\Training a RoboCup Striker Agent via Transferred Reinforcement Learning.\Some("https://link.springer.com/chapter/10.1007%2F978-3-030-27544-0_9")\["s_i", "s_f"]\["same_all"]\["pi"]\["tt"]\\["Custom", "CS", "Tables", "Figures", "Formulas"]\["DDPG"]\["N/A"]\0\1\\["Simulation", "Striker", "RoboCup", "Curriculum", "CNN", "MultiAgent", "Soccer"]\["TD", "MB"]\1\1\1\["USA"]\["Colorado School of Mines"]\["Computer Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2062960870\Preferential exploration method of transfer learning for reinforcement learning in Same Transition Model\Some("https://dblp.uni-trier.de/db/conf/scisisis/scisisis2012.html#TakanoTKT12")\["s_i", "s_f", "s", "levels"]\["multi"]\["rule", "pi"]\["tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Tables", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD"]\1\1\1\["Japan"]\["Mie University"]\["Engineering", "Regional Innovation Studies"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid", "Rooms"]
2834852173\Common Subspace Transfer for Reinforcement Learning Tasks\Some("https://cris.maastrichtuniversity.nl/en/publications/common-subspace-transfer-for-reinforcement-learning-tasks")\["v", "a", "#", "t"]\["lit"]\["fea", "sub"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["FVI"]\["exp"]\1\0\\["2D", "CartPole", "Swingup", "MassSystem", "Simulation", "ClassicControl"]\["TD"]\1\1\1\["Netherlands", "USA"]\["Maastricht University", "Lafayette College"]\[]\1481405077\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["SingleMass", "DoubleMass"], ["Pendulum", "CartpoleSwingup"]]\\3\
2902788990\Cache-Enabled Adaptive Bit Rate Streaming via Deep Self-Transfer Reinforcement Learning\Some("https://dblp.uni-trier.de/db/conf/wcsp/wcsp2018.html#ZhangZLHY18")\[]\["same_all"]\["r", "imitation"]\["tr"]\\["Custom", "CS",  "Formulas", "Pseudo", "Figures", "Tables"]\["DQN", "NAF"]\["N/A"]\0\1\\["Network", "Wireless", "Simulation", "Adaptive", "BitRate", "Streaming", "SelfTransfer", "CacheEnabled"]\["TD"]\1\1\1\["China"]\["Southeast University"]\["Information Science and Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2906852733\Reusing Source Task Knowledge via Transfer Approximator in Reinforcement Transfer Learning\Some("https://www.mdpi.com/2073-8994/11/1/25/pdf")\["s", "s_i", "s_f", "t"]\["diff-it", "lit"]\["fea", "Q"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["TL-ANNA", "PPR","Q"]\["exp"]\1\1\\["2D", "Navigation", "RoboCup", "Simulation", "KeepAway", "ProbabilisticPolicyReuse", "MultiAgent", "3v2", "4v3"]\["TD"]\1\0\0\["China"]\["National University of Defense Technology"]\["Intelligence Science and Technology"]\0\["lib"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2166502700\Intelligent call transfer based on reinforcement learning\Some("https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2000-6.html#JevticS00")\[]\["pure"]\[]\[]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\[]\[]\2\0\\["Call Transfer", "ICT", "Simulation"]\["TD"]\0\1\1\["Croatia"]\["University of Zagreb"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l"]\\\\\
2949169393\Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer\Some("https://openreview.net/pdf?id=BklR5pHhjV")\["t", "s_i", "s_f", "s", "levels"]\["sim2real"]\["distil", "pi_dyn", "SRL"]\["tr"]\\["OSS", "stable-baselines2", "PyBullet", "numpy", "Figures", "Formulas", "Videos"]\["PPO2"]\["N/A"]\0\1\\["2D", "Navigation", "Robotics", "Simulation", "RealWorld"]\["TD"]\1\0\0\["France"]\["Flowers Laboratory", "Softbank Robotics Europe", "Thales"]\["AI Lab", "Theresis Lab"]\0\["lib"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
2973019294\Transferring Human Manipulation Knowledge to Industrial Robots Using Reinforcement Learning\Some("http://www.sciencedirect.com/science/article/pii/S2351978920301372")\["t"]\["same_all"]\["I"]\[]\\["Custom", "CS", "Gym", "Figures", "Formulas", "Tables", "Pseudo"]\["Q"]\[]\0\0\\["Robotics", "Simulation", "RealWorld", "Imitation", "Gazebo"]\["TD"]\1\0\0\["Spain", "Denmark"]\["Mondragon University", "Aalborg University"]\["Robotics and Automation"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2892267807\VPE: Variational Policy Embedding for Transfer Reinforcement Learning\Some("http://export.arxiv.org/pdf/1809.03548")\["t"]\["sim2real"]\["Q", "pi_gen"]\["tr"]\\["Custom", "CS", "Gym", "Figures", "Formulas", "Videos"]\["DQN"]\[]\0\1\\["Robotics", "Simulation", "RealWorld", "MasterQ", "Pendulum", "ClassicControl"]\["TD", "B"]\1\0\0\["Sweden"]\["Royal Institute of Technology", "Örebro University"]\["Robotics, Perception and Learning Lab", "Applied Autonomous Sensor Systems"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2794577523\Cache-Enabled Dynamic Rate Allocation via Deep Self-Transfer Reinforcement Learning.\Some("https://arxiv.org/pdf/1803.11334")\[]\["same_all"]\["r", "imitation"]\["tr"]\\["Custom", "CS",  "Formulas", "Pseudo", "Tables", "Figures"]\["DQN", "NAF"]\["N/A"]\0\1\\["Network", "Wireless", "Simulation", "Adaptive", "BitRate", "Streaming", "SelfTransfer", "CacheEnabled"]\["TD"]\1\1\1\["China"]\["Southeast University"]\["Information Science and Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\2\\
1585019787\Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer\Some("https://128.84.21.199/abs/1412.6451?context=cs.RO")\["s_f", "levels"]\["multi"]\["I"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["Q", "QP"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD", "H", "MB"]\1\0\0\["Germany"]\["Otto-Friedrich-Universität Bamberg"]\["Information Systems and Applied Computer Sciences"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
126751897\Automated transfer in reinforcement learning\Some("https://cris.maastrichtuniversity.nl/portal/en/publications/automated-transfer-in-reinforcement-learning(0a610cee-18d6-4ffe-a803-e6153fc7dc61).html")\["t", "a", "v"]\["diff-no", "lit"]\["Q", "I"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["LSPI", "FQI"]\["exp"]\0\0\\["2D", "Simulation", "MountainCar", "InvertedPendulum", "CartPole", "ClassicControl"]\["TD", "H", "B", "MB", "batch"]\1\0\0\["Netherlands", "USA"]\["Maastricht University", "Lafayette College"]\["Computer Science"]\1\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["InvertedPendulum", "MountainCar"], ["MountainCar", "CartPole"], ["InvertedPendulum", "CartPole"]]\\\
2952460221\Transfer Reinforcement Learning based Framework for Energy Savings in Cellular Base Station Network\Some("https://ieeexplore.ieee.org/document/8738418")\["s_i"]\["same_all"]\["pi"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Actor-Critic"]\["N/A"]\0\0\\["Energy Saving", "Network", "WiFi", "RealWorld", "Simulation"]\["TD"]\1\0\0\["India"]\["Indraprastha Institute of Information Technology"]\["Electronics and Communication Engineering"]\2604038904\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l"]\\[]\\1\
3022965940\5G UDN Proactive Handover with Transferable Reinforcement Learning-based Trajectory Prediction\Some("https://boris.unibe.ch/134014/")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["5G", "ICT", "Handover"]\[]\0\1\0\[]\[]\[]\0\[]\["mag"]\["t", "r", "l"]\[]\[]\\\\\
2997386021\Bayesian Inverse Reinforcement Learning for Demonstrations of an Expert in Multiple Dynamics: Toward Estimation of Transferable Reward\Some("https://www.jstage.jst.go.jp/article/tjsai/35/1/35_G-J73/_pdf")\[]\[]\["r"]\[]\\["Custom", "CS", "Formulas", "Pseudo"]\["Q", "BIRL"]\[]\0\2\\["2D", "Navigation", "Grid", "Simulation"]\["TD"]\1\0\0\["Japan"]\["Chiba University"]\["Urban Environment Systems"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\\\\
2989943922\Helping an Agent Reach a Different Goal by Action Transfer in Reinforcement Learning\Some("http://doi.org/10.1007/978-3-030-35288-2_2")\["s_f"]\["multi"]\["advisor"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Theorem", "Pseudo", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation", "PolicySimilarity"]\["TD", "H", "RRL"]\1\1\1\["Australia"]\["University of Wollongong"]\["Computing and Information Technology"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2100493795\Online collaborative multi-agent reinforcement learning by transfer of abstract trajectories\Some("http://dare.uva.nl/document/2/64097")\["#", "levels", "s_i", "s_f"]\["multi"]\["fea", "AbstractTrajectories"]\["tt"]\\["Custom", "CS", "Multiquest", "Figures", "Pseudo", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Pickup", "Subgoals", "MultiAgent", "Simulation"]\["TD", "H"]\1\0\0\["Netherlands"]\["University of Amsterdam"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k", "s"]\\\\\["Grid", "Maze", "Obstacles"]
2956458483\Global Maximum Power Point Tracking of PV Systems under Partial Shading Condition: A Transfer Reinforcement Learning Approach\Some("https://www.mdpi.com/2076-3417/9/13/2769/pdf")\["t", "s_i"]\["same_all"]\["KM", "Q"]\["j", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["PV", "Power", "Electric", "Efficiency", "SmartEnergy", "MPPT", "Simulation"]\["TD"]\1\0\0\["China"]\["Suzhou Power Supply Company", "Kunming University of Science and Technology", "Shantou University"]\["Industry", "Uni", "Colab"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2997891625\Transfer Reinforcement Learning using Output-Gated Working Memory\Some("https://aaai.org/Conferences/AAAI-20/wp-content/uploads/2020/01/AAAI-20-Accepted-Paper-List.pdf#4442")\["s_i", "s_f"]\["multi"]\["output-gated-memory"]\["j", "tt"]\\["OSS", "Github", "pandas", "Formulas", "Figures", "Tables"]\["DQN", "HRR"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "HRR", "Gates", "Simulation"]\["TD"]\1\0\0\["USA"]\["Middle Tennessee State University"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2987047801\A Deep Reinforcement Learning based Approach to Learning Transferable Proof Guidance Strategies.\Some("https://arxiv.org/abs/1911.02065")\["s", "t"]\["lit"]\["pi"]\["j", "ap"]\\["Custom", "CS", "deepmath", "SciKit-Optimize", "TPTP", "Formulas", "Figures", "Tables"]\["TRAIL"]\["N/A"]\1\1\\["Simulation", "FOL", "FirstOrderLogic", "TheoremProving", "Theorem", "TRAIL", "Proof", "Guidance"]\["TD"]\1\0\0\["USA"]\["Northwestern University","University of Illinois", "MIT-IBM AI Lab", "IBM Research", "The University of Auckland"]\["Uni", "Industry", "Colab"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\2\\
232085181\Transferring Models in Hybrid Reinforcement Learning Agents\Some("https://link.springer.com/content/pdf/10.1007%2F978-3-642-23957-1_19.pdf")\["v", "t"]\["diff-it"]\["model", "r"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables", "RL-Glue"]\["TiMRLA", "Q", "SARSA"]\["sup"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "ClassicControl"]\["TD", "MB"]\1\0\0\["Greece"]\["Aristotle University of Thessaloniki"]\["Informatics"]\3002235425\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\6\
197857547\Qualitative Transfer for Reinforcement Learning with Continuous State and Action Spaces\Some("https://rd.springer.com/chapter/10.1007/978-3-642-41822-8_25")\["t"]\["same_all"]\["model", "pi"]\["tt"]\\["Custom", "CS",  "Formulas", "Pseudo", "Figures"]\["PILCO", "QTL-PILCO"]\["N/A"]\0\0\\["InvertedPendulum", "Simulation", "GuassianProcess", "ClassicControl"]\["TD", "PS", "MB"]\1\0\0\["Mexico"]\["Instituto Nacional de Astrófisica"]\["Optics and Electronics"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2949344914\Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks.\Some("http://arxiv.org/pdf/1906.06576.pdf")\["s_i", "s_f", "#"]\["multi"]\["pri"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures"]\["DDQN", "DQN", "LTS"]\["N/A"]\0\1\\["2D", "Simulation", "Navigation", "Grid"]\["TD"]\1\0\0\["Belgium", "USA"]\["Université Libre de Bruxelles", "Sony Computer Science Laboratories"]\["Industry", "Uni", "Colab"]\0\["all"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
2990376820\Self-Attentional Credit Assignment for Transfer in Reinforcement Learning\Some("https://openreview.net/pdf?id=B1xybgSKwB")\["v", "s_i", "s_f", "t", "r"]\["multi"]\["r", "credit"]\["tt"]\\["Custom", "CS", "Formulas", "Figures"]\["Q", "PPO", "DQN"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "DMLab", "Credit", "Simulation"]\["TD"]\1\0\0\["USA"]\["Google Research"]\["Industry"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\1\\
3008492644\Real–Sim–Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning\Some("https://www.mdpi.com/2076-3417/10/5/1555/pdf")\["t", "s_i", "s_f"]\["multi"]\["RSR", "model"]\["tt"]\\["Custom", "CS", "Gazebo", "TensorFlow", "Gym", "Pseudo", "Formulas", "Figures", "Tables"]\["PPO"]\["N/A"]\0\1\\["Robotics", "Simulation", "RealWorld", "Gazebo", "Manipulation", "Navigation", "3D"]\["TD"]\1\0\0\["China"]\["Chinese Academy of Sciences", "State Key Laboratory of Management and Control for Complex Systems"]\["Institute of Automation"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "s"]\["t", "r", "l", "s"]\\\\\
1803475911\Hierarchical Functional Concepts for Knowledge Transfer among Reinforcement Learning Agents\Some("http://ijfs.usb.ac.ir/article_2113.html")\["s_i", "s_f", "levels"]\["multi"]\["Q", "fea"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Taxi", "Simulation"]\["TD", "H"]\1\0\0\["Iran"]\["University of Tehran"]\["Electrical and Computer Engineering"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid", "Taxi", "Obstacles"]
2980248684\Knowledge Transfer in Reinforcement Learning Agent\Some("http://xplorestaging.ieee.org/ielx7/8850849/8860868/08860881.pdf?arnumber=8860881")\["s_i", "s_f"]\["multi"]\["Q", "pi"]\["j"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Grid"]\["TD", "CBR"]\1\0\0\["Bulgaria"]\["Bulgarian Academy of Sciences"]\["Institute of Robotics"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3026075272\Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning.\None\["t"]\["same_all"]\["WT"]\["j", "tr", "tt"]\\["Custom", "CS", "Gym", "TensorFlow", "Keras", "ChainerRL", "Figures", "Formulas", "Tables"]\["ACER"]\[]\0\1\\["Malware", "Detection", "Simulation"]\["TD"]\1\0\0\["Israel"]\["Ben-Gurion University of the Negev"]\["Software and Information Systems Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\2\\
2891191051\Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation\Some("https://ui.adsabs.harvard.edu/abs/2018arXiv180900770H/abstract")\["#", "v", "t"]\["lit"]\["GAN"]\["tr", "tt"]\\["Custom", "CS", "Gym", "ALE", "Formulas", "Figures", "Tables"]\["DQN"]\["N/A"]\0\1\\["2D", "Games", "VideoGames", "Atari", "Pong", "Simulation"]\["TD"]\1\0\0\["Taiwan"]\["National Taiwan University"]\["Computer Science and Information Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l", "k", "s"]\["t", "r", "l", "k"]\\\2\\
2167778886\Transferring experience in reinforcement learning through task decomposition\Some("https://dl.acm.org/citation.cfm?id=1558109.1558208")\["#"]\["same_all"]\["rule", "advisor"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q"]\["sup"]\0\0\\["2D", "RoboCup", "KeepAway", "Simulation", "MultiAgent", "4v3", "3v2", "5v4", "6v5"]\["TD"]\1\0\0\["Greece"]\["Aristotle University of Thessaloniki"]\["Informatics"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3008744627\Efficient Deep Reinforcement Learning through Policy Transfer\Some("https://www.arxiv-vanity.com/papers/2002.08037/")\["s_i", "s_f", "s", "levels", "t"]\["multi"]\["options"]\["j", "tr", "tt", "ap"]\\["Custom", "CS", "fakeOSS", "Figures", "Formulas", "Pseudo"]\["PTF-A3C", "PTF-PPO", "A3C", "PPO"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Simulation", "Games", "Pinball", "Reacher", "Robotics"]\["TD"]\1\0\0\["China"]\["Tianjin University", "Huawei", "Nanjing University", "Netease", "JD Digits"]\["Intelligence and Computing", "Noahs Ark Lab", "Machine learning"]\3024199149\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\7\
3022749764\Enhancing Transferability of Deep Reinforcement Learning-Based Variable Speed Limitendgraf Control Using Transfer Learning\Some("http://xplorestaging.ieee.org/ielx7/6979/4358928/09090297.pdf?arnumber=9090297")\["t"]\["same_all"]\["nn_weight_links"]\["tt"]\\["Custom", "CS", "Formulas"]\["VSL", "DDQN"]\["N/A"]\0\1\\["Simulation", "Traffic", "SmartTraffic", "SpeedLimit", "Adaptive", "Vehicle"]\["TD"]\1\1\1\["China", "Australia"]\["Southeast University", "University of Tasmania"]\["School of Transportation", "Information and Communication Technology"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2893105945\Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules.\Some("https://export.arxiv.org/pdf/1810.00468")\["s"]\["same_all"]\["pri"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Gym", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD", "B"]\1\0\0\["Greece"]\["Athens University"]\["Economics and Business"]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\2\\
2989941044\Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors.\Some("http://ceur-ws.org/Vol-2491/paper11.pdf")\["t"]\["multi"]\["advisor"]\["j", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["BDPI", "ABDPI", "DQN"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "Rooms", "Simulation"]\["TD"]\1\0\0\["Belgium"]\["Vrije Universiteit Brussel"]\["Computer Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
1604959332\Relational transfer across reinforcement learning tasks via abstract policies.\Some("http://www.teses.usp.br/teses/disponiveis/3/3141/tde-04112014-103827/pt-br.php")\["s_i", "s_f", "levels"]\["multi"]\["Q", "pi_dyn"]\["tt", "ap"]\\["OSS", "Custom", "Figures", "Pseudo", "Formulas", "Tables"]\["SARSA", "Q", "AbsSarsa", "AbsProb-RL", "S2L-RL"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation", "MonteCarlo"]\["RRL", "CBR", "H", "TD", "MB"]\1\0\0\["Brazil"]\["University of Sao Paulo"]\["Computer Engineering"]\2\["all", "lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\["Grid"]
3007833995\Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning\Some("https://dblp.uni-trier.de/db/journals/access/access8.html#CaiSXMC20")\["s"]\["same_all"]\["fea", "Q"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["Q"]\["N/A"]\0\1\\["Underwater", "Image", "Feature", "Extraction", "Target", "Recognition", "AutonomousUnderwaterVehicle", "AUV", "CNN", "Vehicle", "Simulation"]\["TD", "B"]\1\0\0\["China"]\["Henan Institute of Science and Technology", "Shandong University"]\["Artifical Intelligence", "Information Engineering", "Control Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2573943923\Transfer of Reinforcement Learning Negotiation Policies: from Bilateral to Multilateral Scenarios\Some("https://researchportal.hw.ac.uk/en/publications/transfer-of-reinforcement-learning-negotiation-policies-from-bila")\["t", "a"]\["multi"]\["pi"]\["tr"]\\["Custom", "CS"]\[]\[]\0\2\\["Simulation", "Trading", "Settlers"]\["TD", "B"]\1\0\0\["UK"]\["Herriot-Watt University"]\["Interaction Lab"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2804848719\Reinforcement-Learning-Based Personalization of Head-Related Transfer Functions\Some("http://www.aes.org/e-lib/browse.cfm?elib=19563")\[]\["pure"]\[]\[]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\[]\[]\2\2\\["HRTF", "HeadRelatedTransfer", "Personalization", "Simulation"]\["TD"]\1\0\0\["Japan"]\["Nagoaka University of Technology", "National Institute of Technology"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2617832762\State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning\Some("https://export.arxiv.org/pdf/1705.08997")\["s_f"]\["same_all"]\["sub"]\["tr"]\\["Custom", "CS", "Figures", "Formulas"]\["PG", "LSTM"]\["exp"]\0\1\\["Hierarchical", "MetaController", "Attention", "Decomposition", "SubgoalCreation", "2D", "Simulation", "Grid", "Navigation"]\["TD", "H"]\1\0\0\["USA"]\["Georgia Institute of Technology"]\["College of Computing"]\0\["h"]\["mag"]\["t", "r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
2395918031\FCM-Type Co-clustering Transfer Reinforcement Learning for Non-Markov Processes\Some("https://dblp.uni-trier.de/db/conf/iukm/iukm2015.html#NotsuUHUH15")\["t"]\["same_all"]\["FCCM"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["Q", "Q-FCCM"]\["N/A"]\0\0\\["2D", "ClassicControl", "Pendulum", "Simulation"]\["TD"]\1\1\1\["Japan"]\["Osaka Prefecture University"]\["Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2617354312\A survey of transfer learning methods for reinforcement learning\Some("https://cedar.wwu.edu/computerscience_stupubs/5/")\[]\["theory", "survey"]\[]\[]\\["Pseudo"]\[]\[]\2\0\\["theory", "survey"]\[]\1\0\0\["USA"]\["Western Washington University"]\["Computer Science"]\2\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
3004450474\Adapt-to-Learn: Policy Transfer in Reinforcement Learning\Some("https://openreview.net/pdf?id=ryeT10VKDH")\["t"]\["same_all"]\["r", "manifold"]\["j", "tr"]\\["Custom", "CS",  "Formulas", "Pseudo", "Theorem", "Tables", "Lemma"]\["PPO", "ATL"]\["exp"]\0\1\\["Simulation", "Robotics", "AdaptToLearn", "Vehicle", "Aerial", "Drone"]\["TD", "B", "MB"]\1\0\0\["USA"]\["University of Illinois"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "s"]\["t", "r", "l", "s"]\\\1\\
199090187\Transfer between Different Reinforcement Learning Methods\Some("https://rd.springer.com/chapter/10.1007/978-3-642-01882-4_7")\[]\["theory", "bookchapter"]\[]\[]\\[]\[]\[]\2\2\\["Theory", "Bookchapter"]\[]\0\1\0\[]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
166919721\Bayesian methods for knowledge transfer and policy search in reinforcement learning\Some("https://ir.library.oregonstate.edu/downloads/1544bs366")\["s_i", "s_f", "levels", "#"]\["multi"]\["advisor", "pri"]\["tt", "tr", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Pseudo"]\["MBOA", "DynaQ", "BOA", "LSPI", "Q", "CMAC", "BPS"]\["N/A"]\0\0\\["Simulation", "2D", "Navigation", "Cost", "Wargus", "MountainCar", "Acrobot", "CartPole", "Bicycle", "3LinkPlanarArm", "MultiAgent", "MonteCarlo", "ClassicControl", "PredatorPrey"]\["H", "TD", "PS", "B", "MB", "batch"]\1\0\0\["USA"]\["Oregon State University"]\["Computer Science"]\1\["all"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Wargus"]
3006434787\Transferring knowledge as heuristics in reinforcement learning\Some("https://dl.acm.org/doi/10.1016/j.artint.2015.05.008")\["a", "v"]\["diff-it"]\["Q"]\["j"]\\["Custom", "OSS", "Formulas", "Figures", "Tables", "Pseudo"]\["HA-SARSA", "L3-SARSA", "HAQL", "CB-HAQL"]\["sup"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "CaseBased", "ClassicControl"]\["CBR"]\1\0\0\["Brazil", "Spain"]\["Centro Universitário da FE", "Universidade Federal do ABC", "Universitat Autonoma de Barcelona"]\["Artifical Intelligence Research Institute"]\0\[]\["mag", "silva"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[["MountainCar", "MountainCar3D"], ["Acrobot", "RobocubJoints"]]\\\
2796814993\Reinforcement Learning Transfer Using a Sparse-Coded Inter-Task Mapping (extended abstract)\Some("https://cris.maastrichtuniversity.nl/portal/en/publications/reinforcement-learning-transfer-using-a-sparsecoded-intertask-mapping-extended-abstract(e5e0ef42-617c-4e6e-b675-1827079f7fc4).html")\["t", "a", "v"]\["diff-no", "lit"]\["Q", "I"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo"]\["LSPI", "FQI"]\["exp"]\0\0\\["2D", "Simulation", "MountainCar", "InvertedPendulum", "CartPole", "ClassicControl"]\["TD", "batch"]\1\0\0\["Netherlands", "USA"]\["Maastricht University", "Lafayette College"]\[]\126751897\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\3\
2910166048\Knowledge Transfer between Multi-granularity Models for Reinforcement Learning\Some("https://dblp.uni-trier.de/db/conf/smc/smc2018.html#XinTWC18")\["v", "s_i", "s_f"]\["same_all"]\["Q", "options"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures"]\["Q", "MGRL"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD", "H"]\1\0\0\["China"]\["Nanjing University"]\["Control and Systems Engineering", "Management and Engineering"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\\\\\
1604516390\Graph Laplacian based transfer learning in reinforcement learning\Some("https://dl.acm.org/citation.cfm?id=1402821.1402869")\["v", "s", "levels"]\["multi"]\["Q", "pvf"]\["j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q", "LaplacianGraph"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD"]\1\0\0\["Taiwan"]\["National Tsing-Hua University"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\["Grid", "Obstacles", "TwoRoom"]
3016369563\Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning.\Some("http://arxiv.org/pdf/2004.06213.pdf")\["s_i", "s_f", "t"]\["same_all"]\["fea", "model"]\[]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["exp"]\0\1\\["2D", "Navigation", "Grid", "Maze", "Simulation", "Hierarchical", "HolographicReducedRepresentations", "WorkingMemory", "AbstractTaskRepresentations"]\["TD", "H", "B"]\1\0\0\["USA"]\["Middle Tennessee State University"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\2\\
2997729838\An Optimal Transfer of Knowledge in Reinforcement Learning through Greedy Approach\Some("https://dblp.uni-trier.de/db/conf/icccnt/icccnt2019.html#KumariCM19")\["a", "v"]\["diff-it"]\["Q"]\["tt"]\\["Custom", "CS",  "Formulas", "Figures", "Tables"]\["SARSA", "MASTER"]\["sup"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "ClassicControl"]\["TD"]\1\1\1\["India"]\["REC Ambedkar Nagar"]\["Information Technology"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[["MountainCar", "MountainCar3D"]]\\\
2247618789\Reinforcement Learning Transfer Based on Subgoal Discovery and Subtask Similarity\None\["s", "t", "levels"]\["multI"]\["sub", "options"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["Q", "SARSA", "SDHRL"]\["N/A"]\0\0\\["2D", "Navigation", "Maze", "Grid", "Simulation"]\["TD", "H"]\1\0\0\["China"]\["Nanjing University"]\["Computer Science and Technology"]\2150385772\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\3\
101901138\Improving Batch Reinforcement Learning Performance through Transfer of Samples\Some("https://dblp.uni-trier.de/db/conf/stairs/stairs2008.html#LazaricRB08")\["s_i", "s_f", "s"]\["multi"]\["I"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["FQI"]\["N/A"]\0\0\\["2D", "Navigation", "Obstacle", "Samples"]\["TD", "batch"]\1\1\0\["Italy"]\["Politecnico di Milano"]\["IIT-Lab"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2907903452\Simulation and Transfer of Reinforcement Learning Algorithms for Autonomous Obstacle Avoidance\Some("https://dblp.uni-trier.de/db/conf/ias/ias2018.html#LenkHMRS18")\["t"]\["sim2real"]\["pi"]\["j"]\\["Custom", "CS", "TensorFlow", "Keras", "Gym", "Figures"]\["Q", "DQN", "DDPG", "A3C"]\["N/A"]\0\1\\["Robotics", "Mindstorm", "Simulation", "RealWorld", "EV3", "Navigation", "3D", "Driving", "Autonomous", "Vehicles", "Car"]\["TD"]\1\1\1\["Germany"]\["SAP SE", "Duale Hochschule Baden-Württemberg"]\["Computer Science"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2889076726\Transfer of reinforcement learning for a robotic skill\Some("https://aaltodoc.aalto.fi:443/handle/123456789/33782")\["t"]\["sim2real"]\["pi", "curriculum"]\["j", "tt"]\\["Custom", "CS", "LWRSIM"]\["PILCO"]\[]\0\0\\["Robotics", "Mujoco", "RealWorld", "Simulation", "Kuka", "Arm", "LWRSim", "PILCO"]\["TD", "PS", "MB"]\1\0\0\["Finland"]\["Aalto University"]\["School of Electrical Engineering"]\2\["h"]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\\\\\
3016335401\A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System\Some("https://doi.org/10.3390/sym12040631")\["#"]\["multi", "multiagent"]\["pi"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Pseudo", "Tables"]\["A2C", "DDPG", "MDDPG"]\["N/A"]\0\1\\["Simulation", "InvertedPendulum", "RTS", "Starcraft", "Multiagent", "ClassicControl"]\["TD"]\1\0\0\["China"]\["Hubei University of Arts and Science"]\["Computer Engineering"]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3023661178\Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigation\Some("http://arxiv.org/pdf/2004.14684.pdf")\["s", "levels"]\["sim2real", "multi"]\["pi"]\[]\\["Custom", "CS", "Gazebo", "PyTorch", "Gym", "Formulas", "Figures", "Tables"]\["SAC"]\["N/A"]\0\1\\["Robotics", "Gazebo", "DepthBased", "3D", "Navigation", "Simulation", "RealWorld", "Vehicle"]\["TD"]\1\0\0\["France", "Australia"]\["Université Paris Saclay", "Flinders University"]\["Aerospace Lab", "Computer Science, Engineering and Mathematics"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2998972968\Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning\Some("https://thesai.org/Downloads/Volume10No12/Paper_2-Activation_and_Spreading_Sequence_for_Spreading_Activation_Policy.pdf")\["s_i", "s_f"]\["multi"]\["pi", "rule"]\["j", "ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["PRQ"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Simulation"]\["TD", "PS"]\1\0\0\["Japan"]\["Tokyo Polytechnic University", "Tokyo Denki University", "The University of Tokyo"]\["Engineering", "Electronics and Mechatronics", "Information and Communication Engineering", "Precision Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
112583961\Graph Laplacian Based Transfer Learning in Reinforcement Learning (Short Paper)\Some("http://www.ifaamas.org/Proceedings/aamas08/proceedings/pdf/paper/AAMAS08_0286.pdf")\["v", "s", "levels"]\["multi"]\["Q", "pi_gen"]\["j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q", "LaplacianGraph"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD"]\1\0\0\["Taiwan"]\["National Tsing-Hua University"]\["Computer Science"]\1604516390\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\3\
3007745042\Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization.\Some("https://arxiv.org/abs/2002.11635")\["t"]\["sim2real"]\["pi"]\["j"]\\["Custom", "CS", "SurrealAI", "Pseudo", "Formulas", "Figures", "Tables", "Videos"]\["SAC", "PPO", "DDPG"]\[]\0\1\\["Robotics", "Simulation", "RealWorld", "Industry", "OSC"]\["TD", "PS"]\1\0\0\["Germany"]\["Corporate Research KUKA Deutschland GmbH"]\["Industry"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\2\\
201259648\Abstraction and Knowledge Transfer in Reinforcement Learning\Some("https://rd.springer.com/chapter/10.1007/978-3-642-16590-0_3")\[]\["theory", "bookchapter"]\[]\[]\\[]\[]\[]\2\2\\["Theory", "BookChapter"]\[]\1\1\1\[]\[]\[]\0\[]\["mag"]\["t", "r", "l", "k"]\[]\["t", "r", "l", "k"]\\\\\
2897816339\A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem.\Some("https://arxiv.org/pdf/1810.04058")\["t", "s"]\["same_all"]\["Q", "distil"]\["j", "tr", "tt", "ap"]\\["Custom", "OSS", "pandas", "numpy", "Formulas", "Figures"]\["Q", "DiRL"]\["N/A"]\0\0\\["Simulation", "Bike", "Distribution", "Distributed", "Rebalancing", "Mobility"]\["TD"]\1\0\0\["USA"]\["New York University"]\[]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\2\\
3019373547\TRANSFERABLE TRAINING FOR AUTOMATED REINFORCEMENT-LEARNING-BASED APPLICATION-MANAGERS\Some("https://lens.org/007-981-527-649-224")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["ApplicationManager"]\[]\1\0\0\["USA"]\["VMWare Inc"]\["Industry", "Patent"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\\\\
3018700377\Transfer Learning Applied to Reinforcement Learning-Based HVAC Control\Some("http://link.springer.com/content/pdf/10.1007/s42979-020-00146-7.pdf")\["t"]\["same_all"]\["Q"]\["j", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Tables"]\["Q"]\["N/A"]\0\0\\["Simulation", "HVAC", "Power", "SmartEnergy", "Heat", "Ventilation"]\["TD", "B"]\1\0\0\["Ireland"]\["National University of Ireland"]\["Science and Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3005639539\Latent Structure Matching for Knowledge Transfer in Reinforcement Learning\Some("https://doi.org/10.3390/fi12020036")\["s", "levels"]\["multi"]\["Q", "manifold"]\["j", "tt", "ap"]\\["Custom", "CS", "Formulas", "Pseudo", "Gym", "Tables", "Figures"]\["Q", "LSM"]\["N/A"]\0\0\\["Simulation", "MountainCar", "ClassicControl"]\["TD"]\1\0\0\["China"]\["University of Shanghai"]\["Computer Engineering and Science"]\0\["h"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
3002235425\Transferring task models in Reinforcement Learning agents\Some("https://dl.acm.org/doi/10.1016/j.neucom.2012.08.039")\["v", "t"]\["diff-it"]\["model", "r"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures", "Tables"]\["TiMRLA", "Q", "SARSA"]\["sup"]\0\0\\["2D", "3D", "2Dto3D", "MountainCar", "MountainCar3D", "Simulation", "ServerScheduling", "ClassicControl"]\["TD", "MB"]\1\0\0\["Greece", "France"]\["Aristotle University of Thessaloniki", "Université Joseph Fourier"]\["Informatics", "Laboratoire LIG"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2385762538\Knowledge Transfer Method Based on the Qualitative Model in Reinforcement Learning\Some("http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJK201106026.htm")\[]\[]\[]\[]\\[]\[]\[]\2\2\\[]\[]\0\1\0\["China"]\[]\[]\0\[]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\[]\\\\\
2998368022\Decision Method of Policy Reuse Ratio for Transfer Reinforcement Learning\Some("https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1A1-P02/_pdf")\[]\[]\[]\[]\\[]\[]\[]\2\2\\[]\[]\0\1\0\["Japan"]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2997158575\Embodiment Mapping Method between Heterogeneous Robots based on Self-body Representation for Transfer Learning in Reinforcement Learning\Some("https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1A1-P01/_pdf")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["Robotics"]\[]\0\1\1\["Japan"]\[]\[]\0\[]\["mag"]\["t", "r", "l"]\[]\[]\\\\\
2326834951\Reinforcement transfer learning with feature information for robot motion planning\Some("http://library.witpress.com/viewpaper.asp?pcode=ACAR14-033-1")\[]\[]\[]\[]\\[]\[]\[]\2\2\\[]\[]\0\1\0\[]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2131464614\Learning Transfer Automatic through Data Mining in Reinforcement Learning\Some("https://www.ijcaonline.org/archives/volume88/number13/15411-3885")\[]\["same_all"]\["exp"]\["tt"]\\["Custom", "CS", "Pseudo", "Figures"]\["LAT", "Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Grid"]\["TD"]\1\0\0\["Iran"]\["University of Bojnord", "Amirkabir University of Technology"]\["Computer Science"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\["Grid", "Rooms", "NonStandardThreeRoom"]
3010746488\Reinforcement learning-based collision avoidance: impact of reward function and knowledge transfer\Some("https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0890060420000141")\["s_i", "s_f", "levels"]\["multi"]\["r", "pi"]\["tt", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["DQN"]\["N/A"]\0\1\\["2D", "Navigation", "Simulation", "CollisionAvoidance", "Vehicle"]\["TD"]\1\0\0\["USA"]\["University of Southern California"]\["Aerospace and Mechanical Engineering"]\0\["h", "lib"]\["mag"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2986299952\Improving the learning speed in reinforcement learning issues based on the transfer learning of neuro-fuzzy knowledge\Some("https://tjee.tabrizu.ac.ir/article_9441_f0444730a3a1ac375a6c1b54e6330981.pdf")\[]\[]\["Q", "fea"]\[]\\["Custom", "CS"]\["Q"]\[]\0\0\\["Foreign Language"]\["TD"]\1\0\0\["Iran"]\["Science and Arts University"]\["Computer Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
168011852\From Transfer to Scaling: Lessons Learned in Understanding Novel Reinforcement Learning Algorithms\Some("https://www.aaai.org/Papers/Workshops/2008/WS-08-14/WS08-14-005.pdf")\["#", "v"]\["same_all"]\["Q", "scaling"]\["tt", "j"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Simulation", "Grid", "MultiAgent"]\["TD"]\1\0\0\["USA"]\["University of Maryland Baltimore County"]\["Computer Science and Electrical Engineering"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\["Grid"]
3025362191\Accelerating bio-inspired optimizer with transfer reinforcement learning for reactive power optimization\Some("https://dl.acm.org/doi/10.1016/j.knosys.2016.10.024")\["t"]\["same_all"]\["Q"]\["j", "tt", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["ABO"]\[]\0\0\\["RPO", "Simulation", "IEEE118", "IEEE300", "MemoryMatrix", "Decomposition"]\["TD"]\1\1\1\["China"]\["South China University of Technology", "Kunming University of Science and Technology"]\["Electric Power Engineering"]\0\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2604038904\Transfer reinforcement learning framework for energy saving in next generation wireless networks\Some("https://repository.iiitd.edu.in/jspui/handle/123456789/417")\["s_i"]\["same_all"]\["pi"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Actor-Critic"]\["N/A"]\0\0\\["Energy Saving", "Network", "WiFi", "RealWorld", "Simulation"]\["TD"]\1\0\0\["India"]\["Indraprastha Institute of Information Technology"]\["Electronics and Communication Engineering"]\2\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2910425974\Knowledge transfer in deep reinforcement learning\Some("https://addi.ehu.es/handle/10810/30667")\["t", "levels"]\["multi"]\["pi"]\["j", "tt"]\\["Custom", "CS", "TensorFlow", "Figures", "Formulas", "Pseudo", "Keras", "Gym", "Tables"]\["DQN"]\["N/A"]\0\1\\["Simulation", "VideoGames", "Games", "Atari", "SpaceInvaders", "DemonAttack", "Qbert", "CNN"]\["TD"]\1\0\0\["Spain"]\["University of the Basque Country"]\["Computer Engineering and Intelligent Systems"]\2\["h"]\["mag"]\["t", "r", "l", "k"]\[]\[]\\\\\
867213644\Task Localization  Similarity  and Transfer; Towards a Reinforcement Learning Task Library System\Some("http://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1578&context=etd")\["s_i", "s_f", "t", "levels"]\["multi"]\["pi", "pi_fix", "pi_dyn"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Theorem"]\["Q"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Robotics", "NomadII", "Simulation"]\["TD", "H", "B"]\1\0\0\["USA"]\["Brigham Young University"]\["Computer Science"]\2\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\["Grid", "Obstacles"]
2769673101\Policy Forgetting Method with Step Units for Decresing of Negative Transfer in Transfer Learning of Reinforcement learning\Some("https://www.jstage.jst.go.jp/article/jsmermd/2017/0/2017_2P1-F06/_article/-char/ja/")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["Robotics"]\["TD"]\0\1\0\["Japan"]\[]\[]\0\[]\["mag"]\["t", "r", "l"]\[]\[]\\\\\
2975838601\A knowledge transfer combined reinforcement learning method and a learning method applied to autonomous skills of an unmanned vehicle\Some("https://lens.org/133-719-533-054-272")\[]\[]\[]\[]\\["Custom", "CS"]\[]\[]\2\2\\["Patent", "UAV", "Robotics", "Aerial"]\[]\0\0\0\["China"]\["University of Shanghai"]\["Patent"]\0\[]\["mag"]\["t", "r", "l", "k", "s"]\["t", "r", "l", "k", "s"]\[]\\\\\
2967023422\Aircraft full-automatic pneumatic optimization method based on reinforcement learning and transfer learning\Some("https://lens.org/148-968-536-858-673")\[]\[]\[]\[]\\["Custom", "CS", "Formulas"]\[]\[]\2\2\\["Patent", "Aircraft", "Pneumatic", "Robotics", "Aerial"]\[]\0\0\0\["China"]\["Tsinghua University"]\["Patent"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\\\\
2091079925\Tactual-kinesthetic feedback from manipulation of visual forms and nondifferential reinforcement in transfer of perceptual learning\Some("https://philpapers.org/rec/BENTFF-2")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\1\1\0\["USA"]\[]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2970910303\Human Sub-goal Transfer in Hierarchical Reinforcement Learning\Some("https://confit.atlas.jp/guide/event/jsai2019/subject/1Q2-J-2-02/detail")\[]\["same_all"]\["options"]\[]\\["Custom", "CS", "Pseudo", "Tables", "Figures", "Formulas"]\["Q"]\[]\0\0\\["SubGoal", "Transfer", "Simulation"]\["H", "TD"]\1\0\0\["Japan"]\["SOKENDAI", "National Institute of Informatics"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\[]\\\\\
3005714659\Transfer Learning for Reinforcement Learning Domains: A Survey\Some("https://dl.acm.org/doi/10.5555/1577069.1755839")\[]\["theory", "survey"]\[]\[]\\[]\[]\[]\2\2\\["Theory", "survey"]\[]\1\0\0\["USA"]\["University of Southern California", "The University of Texas"]\["Computer Science"]\0\[]\["mag", "lazaric", "silva", "cur", "zhu"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k", "s"]\\\\\
2914188912\Transfer reinforcement learning for task-oriented dialogue systems\Some("http://repository.ust.hk/ir/Record/1783.1-92234")\["s", "t"]\["multi"]\["WT", "special_weights"]\["ap"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Pseudo"]\["Q", "PETAL", "PROMISE"]\["exp"]\0\0\\["Task", "Oriented", "Dialogue", "System", "CNN", "NLP", "Simulation"]\["TD", "H", "B"]\1\0\0\["China"]\["Hong Kong University of Science and Technology"]\["Computer Science and Engineering"]\1\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2945850821\Improving Deep Reinforcement Learning Using Graph Convolution and Visual Domain Transfer\Some("https://tigerprints.clemson.edu/all_dissertations/2268/")\["s"]\["same_all"]\["Q"]\["tr", "j"]\\["Custom", "CS",  "Formulas", "Pseudo", "Theorem", "TensorFlow", "Tables", "Figures"]\["GVIN", "VIN", "DQN"]\[]\0\1\\["2D", "Simulation", "Graph", "IrregularGraph", "RoadNetwork", "Navigation", "CARLA", "CNN", "MonteCarlo"]\["TD", "MB"]\1\0\0\["USA"]\["Clemson University"]\["Computer Engineering"]\1\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\\
2886781774\Transfer Surface for Transfer Learning in Reinforcement Learning\Some("https://confit.atlas.jp/guide/event/ssii2018/subject/IS2-27/detail")\[]\[]\[]\[]\\[]\[]\[]\2\2\\[]\[]\0\1\0\["Japan"]\["Tokyo Polytechnic University", "The University of Tokyo", "Tokyo Denki University"]\[]\0\[]\["mag"]\["t", "r", "l"]\[]\[]\\\\\
1483433356\Skill Transfer of a Mobile Robot Obtained by Reinforcement Learning to a Different Mobile Robot\Some("https://dblp.uni-trier.de/db/series/sci/sci266.html#KameiI10")\["t"]\["same_all"]\["Q"]\["j"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["Simulation", "RealWorld", "Robot", "Navigation", "2D", "GeneticAlgorithm", "Vehicle", "MultiAgent"]\["TD", "H", "B"]\1\1\1\["Japan"]\["Nishinippon Institute of Technology", "Kyushu Institute of Technology"]\["Electrical, Electronics and Information Technology"]\0\[]\["mag"]\["t", "r", "l", "s"]\["t", "r", "l", "s"]\["t", "r", "l", "s"]\\\\\["Obstacles"]
2770456017\Multi-Agent Reinforcement Learning: - Learning Effects by Transfer Learning  Eligibility Trace and Sensor Range -,,,―転移学習，適格度，センサ範囲の学習効果―\Some("https://www.jstage.jst.go.jp/article/jsmermd/2017/0/2017_2A1-G09/_article/-char/ja/")\[]\["multi", "multiagent"]\["rule"]\[]\\["Custom", "CS"]\["Q"]\[]\2\0\\["Simulation", "NarrowPath", "Rules"]\["TD"]\0\1\0\["Japan"]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
2778259398\Evolutionary transfer learning for complex multi-agent reinforcement learning systems\Some("https://dr.ntu.edu.sg/handle/10356/72997")\["s_i", "s_f", "levels"]\["multi"]\["Q", "advisor"]\["ap"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["Q", "FALCON", "BP", "eTL", "PTL", "AE-AVG", "MAS"]\["N/A"]\0\1\\["2D", "3D", "Simulation", "Games", "Navigation", "Minefield", "MinefieldNavigation", "UnrealTournament", "MultiAgent"]\["TD", "MB"]\1\0\0\["Singapore"]\["Nanyang Technological University"]\["Interdisciplinary Graduate School"]\1\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
2292364897\Transfer in reinforcement learning\Some("https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561")\["t"]\["same_all"]\["Q", "pri"]\["j", "tr", "tt"]\\["Custom", "CS", "Pseudo", "Formulas", "Theorem", "Figures", "Tables", "Lemma"]\["Q", "SARSA"]\[]\0\0\\["Simulation", "CartPole", "MountainCar", "Acrobot", "Pinball", "ClassicControl"]\["TD", "MB"]\1\0\0\["UK"]\["University of Aberdeen"]\["Computer Science"]\1\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "s", "k"]\["t", "r", "l", "s", "k"]\\\\\
1511270451\Graph Laplacian Based Transfer Learning Methods in Reinforcement Learning\Some("http://cdn.intechweb.org/pdfs/10989.pdf")\["v", "s", "levels", "s_i", "s_f"]\["multi"]\["Q", "pvf"]\["j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q", "LaplacianGraph"]\["exp"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD"]\1\0\0\["Taiwan"]\["National Tsing-Hua University"]\["Computer Science"]\1604516390\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\\\8\["Grid", "Maze"]
2559014014\Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems\Some("http://www.tara.tcd.ie/handle/2262/77886")\[]\["same_all"]\["Q", "r", "imitation"]\["j", "tt", "ap"]\\["Custom", "CS", "Pseudo", "Figures", "Tables", "Formulas", "GridLab"]\["Q", "DistributedW"]\["exp"]\0\0\\["MultiAgent", "Simulation", "MountainCar", "CartPole", "GridLab","SmartGrid", "Parallel", "ClassicControl"]\["TD"]\1\0\0\["Ireland"]\["University of Dublin"]\["Computer Science"]\1\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2473744971\Policy abstraction for transfer learning using learning vector quantization in reinforcement learning framework\Some("http://umpir.ump.edu.my/id/eprint/13521/")\["s_i", "s_f", "levels", "t"]\["multi"]\["pri"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables", "Pseudo"]\["Q", "LVQ"]\["N/A"]\0\0\\["2D", "Navigation", "Grid", "Maze", "Simulation"]\["TD"]\1\0\0\["Japan"]\["Kyushu University"]\["Electrical and Electronic Engineering"]\1\["h"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2745684090\Effects of catechol-O-methyltransferase on reinforcement learning and mesolimbic dopamine transmission\Some("https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.714050")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Neurosciences", "neurology"]\[]\1\0\0\["UK"]\["University of Oxford"]\[]\1\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2613227537\Sample Complexity Reduction in Reinforcement Learning by Transferred Transition and Reward Probability\Some("https://www.ieice.org/ken/paper/20131113eB7B/eng/")\["t"]\["multi"]\["fea", "r"]\[]\\["Custom", "CS"]\["TR-MAX"]\[]\0\0\\["MDP", "PAC-MDP"]\["TD", "MB"]\0\1\0\["Japan"]\["Tohoku University"]\["Graduate School of Information Sciences"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\\\\
2523226036\Integrating policy transfer  policy reuse and experience replay in speeding up reinforcement learning of the obstacle avoidance task\Some("http://erepository.uonbi.ac.ke/handle/11295/90177")\["a"]\["multi"]\["pi", "experiencereplay"]\["j", "tt", "ap"]\\["Custom", "CS", "Figures", "Formulas", "Tables", "Pseudo"]\["SARSA"]\["sup"]\0\0\\["3D", "Navigation", "Robotics", "Obstacle", "Avoidance", "ExperienceReplay", "Vehicle", "Simulation"]\["TD", "H"]\1\0\0\["Kenia"]\["University of Nairobi"]\["Computing and Informatics"]\1\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\\\
1470793189\Transferring Human Navigation Behaviors Into Robot Planning using Inverse Reinforcement Learning Techniques\Some("https://idus.us.es/xmlui/handle/11441/26823")\["s"]\["same_all"]\["I"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["GPIRL", "PRX"]\["N/A"]\0\0\\["Inverse", "IRL", "RealWorld", "Simulation", "Navigation", "Human", "Demonstration"]\["TD", "B"]\1\0\0\["Spain"]\["University of Seville"]\["Systems Engineering"]\1\["all"]\["mag"]\["t", "r", "l"]\[]\["t", "r", "l", "k", "s"]\\\\\["RealRobot"]
1975326778\A Container Transfer Scheduling Using Reinforcement Learning\Some("https://www.jstage.jst.go.jp/article/ieejias/123/10/123_10_1111/_pdf")\[]\["pure"]\[]\[]\\[]\[]\[]\2\2\\["BlockStacking", "ContainerTerminal", "ContainerScheduling", "Simulation"]\[]\1\0\0\["Japan"]\["Hiroshima Research & Developement Center"]\["Industry"]\0\[]\["mag"]\["t", "r", "l"]\["r", "l"]\[]\\\\\
2327840474\The Knowledge Transfer Methods and Its Application in Robot Based on Subtask Hierarchical Reinforcement Learning\Some("http://www.aicit.org/aiss/global/paper_detail.html?jname=AISS&q=1770")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Robotics", "Cleaning", "RealWorld"]\[]\0\1\0\[]\[]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
1490764095\An Intelligent Marshaling Based on Transfer Distance of Containers Using a New Reinforcement Learning for Logistics\Some("http://cdn.intechopen.com/pdfs/13071/InTech-An_intelligent_marshaling_based_on_transfer_distance_of_containers_using_a_new_reinforcement_learning_for_logistics.pdf")\[]\["pure"]\[]\[]\\[]\[]\[]\2\2\\["BlockStacking", "ContainerTerminal", "Simulation"]\[]\1\0\0\["Japan"]\["Osaka Institute of Technology"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2340098619\A New Reinforcement Learning Method for Train Marshaling Based on the Transfer Distance of\Some("http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp80-85.pdf")\[]\["pure"]\[]\[]\\[]\[]\[]\2\2\\["BlockStacking", "ContainerTerminal", "ContainerScheduling", "Locomotive", "Simulation"]\[]\1\0\0\["Japan"]\["Osaka Institute of Technology"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2094258649\Acquisition and transfer of conceptual learning by normals and retardates: the effects of modeling  verbal cues  and reinforcement.\Some("http://www.ncbi.nlm.nih.gov/pubmed/4429530")\[]\[]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\0\1\0\["USA"]\["University of Georgia"]\[]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\[]\\\\\
2182493345\Task Similarity Measures forTransferin Reinforcement LearningTask Libraries\None\["s_i", "s_f", "levels"]\["multi"]\["fea"]\["ap"]\\["Custom", "CS", "Figures", "Formulas", "Tables"]\["Q"]\["N/A"]\0\0\\["Simulation", "2D", "Navigation", "Grid", "MovingGoal"]\["TD"]\1\0\0\["USA"]\["Brigham Young University"]\["Computer Science"]\1582256513\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\3\
2184525547\ERRORLESSLEARNING:REINFORCEMENTCONTINGENCIESAND STIMULUSCONTROLTRANSFERINDELAYED PROMPTING\None\[]\["Neurology"]\[]\[]\\[]\[]\[]\2\2\\["Neurology"]\[]\1\0\0\["USA"]\["Harvard Medical School"]\["Neurology"]\0\[]\["mag"]\[]\[]\[]\\\\\
266489658\PREDICTION OF MIXED SCHEMA LEARNING IN A REPRODUCTION TASK. THE EFFECTS OF INCIDENTAL LEARNING AND REINFORCEMENT ON SCHEMATA LEARNING AND SCHEMATA TRANSFER. INTERIM REPORT.\Some("https://eric.ed.gov/?id=ED016261")\[]\["psychology"]\[]\[]\\[]\[]\[]\2\2\\["Psychology"]\[]\1\0\0\["USA"]\["U.S. DEPARTMENT OFHEALTH, EDUCATION, AND WELFARE"]\[]\0\[]\["mag"]\[]\[]\[]\\\\\
24477102\Training and Tracking in robotics\Some("https://www.ijcai.org/Proceedings/85-1/Papers/129a.pdf")\["t"]\["same_all"]\["Q"]\["tt"]\\["Custom", "CS", "Formulas"]\["Q"]\["N/A"]\0\0\\["CartPole", "Simulation", "ClassicControl"]\["TD"]\1\0\0\["UK"]\["G T E Laba","University of Massachusetts"]\["Computer Science"]\0\["h"]\["taylor"]\[]\["r", "l"]\["l"]\\\\\
1584313244\Vision-based Behavior Acquisition for a Shooting Robot by using RL by Asada\Some("http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.54.6159&rep=rep1&type=pdf")\["s_i"]\["same_all"]\["Q"]\["tt"]\\["Custom", "CS", "Figures", "Formulas", "Pseudo", "Tables"]\["Q"]\["N/A"]\0\0\\["Robotics", "Navigation", "RealWorld"]\["TD"]\1\0\0\["Japan"]\["Osaka University"]\["Mechanical Engineering"]\0\["h"]\["taylor"]\["r", "l"]\["r", "l"]\["r", "l"]\\\\\["RealRobot"]
2012036715\Transfer of learning by composing solutions of elemental sequential tasks by Satinder Singh\Some("https://link.springer.com/content/pdf/10.1007/BF00992700.pdf")\["r"]\["same_all"]\["Q"]\["ap", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q", "CQL"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["TD"]\1\0\0\["USA"]\["University of Massachusetts"]\["Computer Science"]\0\["all"]\["taylor"]\["t", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\["Grid", "Obstacles"]
2117629901\A comparison of direct and model-based RL\\["r"]\["same_all"]\["model"]\["j", "ap", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["Q", "CMAC"]\["N/A"]\0\0\\["Robotics", "Simulation"]\["MB"]\1\1\1\["USA"]\["Georgia Institute of Technology"]\["College of Computing"]\0\["all"]\["taylor"]\["r", "l"]\["r", "l"]\["t", "r", "l"]\\\\\
1822705290\Effective control knowledge transfer through learning skill and representation hierarchies\\["r"]\["same_all"]\["options"]\["tt"]\\["Custom", "CS", "Formulas", "Figures", "Web", "Dead", "UrbanCombatTestbed"]\["Q"]\["N/A"]\0\0\\["3D", "Navigation", "Simulation"]\["H"]\1\0\0\["USA"]\["West Chester University of Pennsylvania", "The University of Texas"]\["Computer Science", "Computer Science and Engineering"]\0\["h"]\["taylor"]\["t", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\
2089561656\State Abstraction for programmable reinforcement learning\\["r", "s_i", "s_f"]\["same_all"]\["pi"]\["tr"]\\["Custom", "CS", "Figures", "Formulas", "Pseudo", "Tables", "Theorem"]\["Q", "MAXQ", "Alisp"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Taxi", "Simulation"]\["H"]\1\0\0\["USA"]\["UC Berkley"]\["Computer Science"]\0\["h"]\["taylor"]\["r", "l"]\["t", "r", "l"]\["t", "r", "l"]\\\\\["Taxi", "Grid"]
1612195517\Relativized Options: Choosing the right transformation\\["t", "s_i", "s_f"]\["same_all"]\["options"]\["tr"]\\["Custom", "CS", "Formulas", "Figures", "Web", "Dead"]\["Q", "SMDP-Q"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Collect", "Simulation"]\["TD", "H"]\1\0\0\["USA"]\["University of Massachusetts"]\["Computer Science"]\0\["h"]\["taylor"]\[]\["l"]\["t", "r", "l"]\\\\\["Grid", "Rooms"]
1607318605\Proto-transfer learning in Markov decision process using spectral methods\\["r", "s_i", "s_f"]\["same_all"]\["pvf"]\["tt"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "Tables"]\["Q", "PVF"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["batch"]\1\0\0\["USA"]\["University of Massachusetts"]\["Computer Science"]\0\["h"]\["taylor"]\[]\["t", "r", "l"]\["t", "r", "l"]\\\\\["Grid", "TwoRoom", "Rooms"]
2165792602\Improving action selection in MDP’s via knowledge transfer\Some("https://www.aaai.org/Papers/AAAI/2005/AAAI05-162.pdf")\["s_f", "t"]\["same_all"]\["A"]\["tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Lemma", "Theorem"]\["Q", "RTP"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["mod"]\["taylor"]\["k", "t"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
1598748993\Structure in the space of value functions\Some("https://link.springer.com/content/pdf/10.1023/A:1017944732463.pdf")\["s_f"]\["multi"]\["sub"]\["j", "tr"]\\["Custom", "CS", "Figures", "Formulas"]\[]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["TD", "H"]\1\0\0\["UK"]\["University of Edinburgh"]\["Computational Neuroscience"]\0\["all"]\["taylor"]\["r", "l"]\["r", "l"]\["r", "l", "k"]\\\\\["Grid", "Maze"]
1974043469\Probabilistic Policy Reuse in a Reinforcement LearningAgent\Some("https://www.researchgate.net/profile/Fernando_Fernandez9/publication/221455416_Probabilistic_policy_reuse_in_a_reinforcement_learning_agent/links/0deec5188b4c49dce6000000.pdf")\["s_i", "s_f"]\["multi"]\["pi"]\["tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["TD"]\1\0\0\["USA"]\["Carnegie Mellon University"]\[]\0\["lib"]\["taylor"]\["r", "l"]\["l"]\["t", "r", "l", "k"]\\\\\["Grid", "Rooms"]
2042357378\Multitask reinforcement learning on the distribution of MDPs\Some("http://fumihide-tanaka.org/lab/content/files/research/Tanaka_CIRA-03.pdf"9\["t"]\["multi"]\["Q"]\["j", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Q", "BPSb"]\["N/A"]\0\0\\["2D", "Grid", "Navigation", "Simulation"]\["TD"]\1\0\0\["Japan"]\["Tokyo Institute of Technology"]\["Computational Intelligence and Systems Science"]\0\["all"]\["taylor"]\["r", "l"]\["r", "l"]\["t", "r", "l"]\\\\\["Grid", "Maze"]
1234567\Model Transfer for Markov Decision Tasks via Parameter Matching\Some("https://d1wqtxts1xzle7.cloudfront.net/46415887/sunmola.pdf?1465736901=&response-content-disposition=inline%3B+filename%3DModel_transfer_for_Markov_decision_tasks.pdf&Expires=1600942573&Signature=atTnvjL7jeDD4y14s3BhAh7tAjkakWokPE5bPdMQ1LlMBrQZXyC9MvdejtGlscqunfUu7Z8chRUA2Q3xHD7ru7GO1XbeHS5XdJjq~9BMj2FO7GBZPbl4UK2n9ISGPh6~HkrfSx3A~lrBQsezoRXsk13B7J4yLO2jrb0ATOwb4H3sxUaqFz5kpD1cmH-noDDVg5rFvxNFe7VyAWlt5MP-j-47DYyrhfGOFfDV5L5xLuci1~t50l6Mvh4ybKgGNQQBCo1laKi2AHhrZjdlMIa1o2yEGqNez45exaJBR3PM1cD6NPkMxD1XQqzeDgAN1CS5DFRu5EUz7Z0GYOZhrooT1Q__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA")\["t"]\["multi"]\["pri"]\["j", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Tables"]\["Bayesian"]\["N/A"]\0\0\\["MDP"]\["B"]\1\0\0\["UK"]\["University of Birmingham", "University Hospital Birmingham"]\["Computer Science"]\0\["all"]\["taylor"]\["t"]\["t", "k"]\["t", "r", "l", "k"]\\\\\
2169743339\Multi-task reinforcement learning: a hierarchical Bayesian approach\Some("http://web.engr.oregonstate.edu/~tadepall/papers/mtrl-icml07.pdf")\["r", "s_f"]\["multi"]\["pri"]\["j", "tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["Bayesian"]\["N/A"]\0\0\\["2D", "Navigation", "Maze", "Grid"]\["B"]\1\0\0\["USA"]\["University of Massachusetts"]\["Electrical Engineering and Computer Sciences"]\0\["all"]\["taylor"]\["r", "l"]\["r", "l"]\["t", "r", "l", "k"]\\\\\
7654321\Transferring state abstractions between MDPs\Some("https://www.academia.edu/download/43419216/transferWorkshop.pdf")\["r", "s"]\["multi"]\["fea"]\["tt"]\\["Formulas", "Figures", "Theorem"]\["GATA"]\["N/A"]\0\0\\["MDP"]\["any"]\1\0\0\["USA"]\["Rutgers University"]\["Computer Science"]\0\["all"]\["taylor"]\["t"]\["t", "l", "k"]\["t", "r", "l", "k"]\\\\\
2106953752\General game learning using knowledge transfer\Some("https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-107.pdf")\["a", "v"]\["diff-no"]\["fea"]\["ap", "j", "tr"]\\["Custom", "CS", "Formulas", "Figures", "Tables"]\["Q"]\["N/A"]\0\0\\["Games", "GGP", "BoardGames", "Connect3", "CaptureGo", "Othello", "Simulation", "Games"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor"]\["t", "l", "k"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2134153324\Generalizing plans to new environments in relational MDPs\Some("http://www.cs.ucf.edu/~gitars/cap6938/guestrin03generalizing.pdf")\["#"]\["diff-no"]\["Q"]\["j"]\\["Custom", "CS", "Figures", "Formulas", "Videos", "Theorem", "Lemma"]\["CPLEX"]\["N/A"]\0\0\\["RTS", "Freecraft", "Games", "VideoGames", "Simulation"]\["LP"]\1\0\0\["USA"]\["Stanford University"]\["Computer Science"]\0\["h"]\["taylor"]\[]\[]\["r", "l"]\\\\\
2166798247\Transfer learning in real-time strategy games using hybrid CBR/RL\Some("https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-168.pdf")\["#"]\["diff-no"]\["Q"]\["j", "tr"]\\["Custom", "CS", "Figures", "Tables", "Formulas"]\["Q"]\["N/A"]\0\0\\["RTS", "MadRTS", "Games", "VideoGames", "Simulation"]\["TD", "CBR"]\1\0\0\["USA"]\["Georgia Institute of Technology"]\["College of Computing"]\0\["h"]\["taylor"]\["t", "l", "rl"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\\
2122982548\Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression\Some("https://www.aaai.org/Papers/AAAI/2005/AAAI05-129.pdf")\["a", "r", "v"]\["diff-it"]\["rule", "advisor"]\["j", "tr"]\\["Custom", "CS", "Formulas", "Figures"]\["Q"]\["sup"]\0\0\\["2D", "RoboCup", "BreakAway", "Simulation", "MultiAgent", "3v2"]\["TD"]\1\0\0\["USA"]\["University of Minnesota", "University of Wisconsin"]\["Computer Science"]\0\["h"]\["taylor"]\["r", "l", "k"]\["r", "l", "k"]\["r", "l", "k"]\\\\\
1506146479\Skill acquisition via transfer learning and advice taking\Some("https://link.springer.com/content/pdf/10.1007/11871842_41.pdf")\["a", "r", "v"]\["diff-it"]\["rule", "advisor"]\["j", "tr"]\\["Custom", "CS", "Aleph", "Pseudo", "Tables", "Figures", "Formulas"]\["Q"]\["sup"]\0\0\\["2D", "RoboCup", "BreakAway", "Simulation", "MultiAgent", "4v3", "3v2", "MoveDownfield", "KeepAway"]\["TD"]\1\0\0\["USA"]\["University of Minnesota", "University of Wisconsin"]\["Computer Science"]\0\["h"]\["taylor"]\["t", "l", "s"]\["t", "r", "l", "s"]\["t", "r", "l", "s", "k"]\\[["KeepAway4v3", "Breakaway3v2"], ["MoveDownfield3v2", "Breakaway3v2"]]\\\
2123995443\Graph-based domain mapping for transfer learning in general games\Some("https://link.springer.com/content/pdf/10.1007/978-3-540-74958-5_20.pdf")\["a", "v"]\["lit"]\["Q"]\["j", "tr"]\\["Custom", "CS", "Pseudo", "Figures", "Formulas", "Tables", "Theorem"]\["Q", "RuleGraph"]\["T"]\0\0\\["GGP", "Simulation", "Games", "BoardGames", "MiniChess"]\["TD"]\1\0\0\["USA"]\["The University of Texas"]\["Computer Science"]\0\["h"]\["taylor"]\["t", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\[["TicTacToe", "MiniChess"], ["Checkers", "MiniChess"], ["MiniChess4", "MiniChess5"]]\\\
203338875\An experts algorithm for transfer learning\Some("https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-172.pdf")\["a", "v"]\["lit"]\["N/A"]\["j"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures", "Theorem"]\["SARSA", "AtEasel"]\["Ma", "svg", "exp"]\0\0\\["2D", "RoboCup", "BreakAway", "Simulation", "MultiAgent"]\["all"]\1\0\0\["USA"]\["University of Michigan"]\["Computer Science and Engineering"]\0\["h"]\["taylor"]\["t", "l"]\["t", "l", "k"]\["t", "r", "l", "k"]\\[["RoboCup3v2", "RoboCup4v2"]]\\\
2138412825\Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines (abstract)\Some("https://research.tue.nl/en/publications/automatically-mapped-transfer-between-reinforcement-learning-task-2")\["a", "t", "r"]\["lit"]\["pi"]\["j", "tt"]\\["Custom", "CS", "Formulas", "Pseudo", "Figures"]\["RBM"]\["exp"]\1\1\\["Simulation", "MountainCar", "CartPole", "RBM", "ClassicControl"]\["TD"]\1\0\0\["Netherlands", "USA"]\["Maastricht University", "Washington State University"]\["Knowledge Engineering", "Electrical Engineering and Computer Science"]\0\["all"]\["mag"]\["t", "r", "l"]\["t", "r", "l"]\["t", "r", "l", "k"]\\[["Inverted Pendulum", "CartPole"], ["MountainCar", "CartPole"]]\\3\
2887657794\Self-organizing maps for storage and transfer of knowledge in reinforcement learning (abst)\Some("https://doi.org/10.1177/1059712318818568")\["s_i", "s_f"]\["same_all"]\["Q"]\["tr"]\\["Custom", "CS", "Pseudo", "Formulas", "Figures"]\["SOM", "Q"]\["SOM"]\0\0\\["SOF", "SelfOrganizingMap", "2D", "Navigation", "Simulation"]\["TD"]\1\0\0\["Singapore"]\["Singapore University of Technology and Design"]\["Computer Science"]\0\["lib"]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k"]\\\\3\
2959558594\Credit Assignment as a Proxy for Transfer in Reinforcement Learning.\Some("https://arxiv.org/abs/1907.08027")\["v", "s_i", "s_f", "t", "r"]\["multi"]\["r", "credit"]\["tt"]\\["Custom", "CS", "Formulas", "Figures"]\["Q", "PPO", "DQN"]\["N/A"]\0\1\\["2D", "Navigation", "Grid", "DMLab", "Credit", "Simulation"]\["TD"]\1\0\0\["USA"]\["Google Research"]\["Industry"]\0\[]\["mag"]\["t", "r", "l"]\["t", "r", "l", "k"]\["t", "r", "l", "k", "s"]\\\1\3\