Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

Minimal and Clean Reinforcement Learning Examples

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

StarCraft II - pysc2 Deep Reinforcement Learning Examples

Deep Reinforcement Learning based Trading Agent for Bitcoin

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

Deep Q-learning for playing flappy bird game

Implementations of Reinforcement Learning Models in Tensorflow

Deep Q-learning for playing tetris game

Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

Deep Q-Learning Network in pytorch (not actively maintained)

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation net…

Forex trading simulator environment for OpenAI Gym, observations contain the order status, performance and timeseries loaded from a CSV file containing rates and indicators. Work In Progress

A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.

Machine Learning and having it Deep and Structured (MLDS) in 2018 spring

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning (DRL) for Mobile Edge Computing (MEC) | This algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks.

A simple example of how to implement vector based DQN using PyTorch and a ML-Agents environment