An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
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Updated
Jul 26, 2024 - Python
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Implementation of "Episodic Memory in Lifelong Language Learning"(NeurIPS 2019) in Pytorch
(TNNLS) Prioritized Experience-Based Reinforcement Learning with Human Guidance for Autonomous Driving
1'st Place approach for CVPR 2020 Continual Learning Challenge
All in one - Everything useful about Aircrack-ng
Reinforcement Learning - Implementation of Exercises, algorithms from the book Sutton Barto and David silver's RL course in Python, OpenAI Gym.
Use Deep Q-Learning model to optimize energy consumption of a data center
Implementation of HindSight Experience Replay paper with Pytorch
Repository containing code for the paper "Meta-Learning with Sparse Experience Replay for Lifelong Language Learning".
Prioritized Sequence Experience Replay
Framework for developing Actor-Critic deep RL algorithms (A3C, A2C, PPO, GAE, etc..) in different environments (OpenAI's Gym, Rogue, Sentiment Analysis, Car Controller, etc..) with continuous and discrete action spaces.
RBDoom is a Rainbow-DQN based agent for playing the first-person shooter game Doom
Off-Policy Correction for Actor-Critic Algorithms in Deep Reinforcement Learning
Train an agent using RL to navigate (and collect bananas) in a large, square world
RL with OpenAI Gym
Distributed RL platform with modified IMPALA architecture. Implements CLEAR, LASER V-trace modifications along with Attentive and Elite sampling experience replay methods.
M.Sc. thesis on Continual Learning for Non-Autoregressive Neural Machine Translation
A reinforcement learning agent trained without prior human knowledge
A multi agent reinforcement learning environment where two agents controlled by DRQNs play a custom version of the pursuit-evasion game.
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