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Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

  •    Jupyter

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.

pytorch-ddpg-naf - Implementation of algorithms for continuous control (DDPG and NAF).

  •    Python

This repository is deprecated and is no longer maintaned. Please see a more recent implementation of RL for continuous control at jax-sac. Reimplementation of Continuous Deep Q-Learning with Model-based Acceleration and Continuous control with deep reinforcement learning.

jaxrl - Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces

  •    Jupyter

The goal of this repository is to provide simple and clean implementations to build research on top of. Please do not use this repository for baseline results and use the original implementations instead (SAC, AWAC, DrQ). If you want to run this code on GPU, please follow instructions from the official repository.

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