Some resources for Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL)：

Key Papers in Deep RL

Important books

Richard S. Sutton and Andrew G. Barto  – Reinforcement Learning: An Introduction

Might be the most important book in the field of RL. In the link are two editions, one is from the year 1998 and the other 2017. If you have interest, you can also visit Richard Sutton’s own homepage (LINK)

 

Platforms

OpenAI Gym

Official Documentation

An open-source python framework for developing and evaluating reinforcement learning algorithms.

 

Online articles

Deepmind：Deep Reinforcement Learning

Deepmind official website with introduction of DRL and their latest research results.

 

Yuxi Li：DEEP REINFORCEMENT LEARNING: AN OVERVIEW

 

Moustafa Alzantot: Deep Reinforcement Learning Demystified (Episode 0)

A short introduction of Reinforcement learning(RL), DRL as well as OpenAI Gym. You can also read the followed episodes 1, 2 …

 

A series of 8 articles regarding reinforcement learning introductions

 

 Flood Sung 深度增强学习前沿算法思想

原载于《程序员》杂志2017年1月刊，介绍了深度增强学习的DQN、A3C和UNREAL算法的发展历程。

Videos

David Silver: Deep Reinforcement Learning Tutorial

2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Edmonton 2015

 

UC Berkeley:CS 294: Deep Reinforcement Learning, Fall 2017

UCL Course on RL （David Silver）

 

DRL papers

Medium-Josh-Deep Reinforcement Learning — Papers

Github-junhyukoh-Deep Reinforcement Learning — Papers

 

Selected Open Source Codes

Horizon: The first open source reinforcement learning platform for large-scale products and services