[DL] Awesome Reinforcement Learning

Awesome Reinforcement Learning

A curated list of resources dedicated to reinforcement learning.

https://github.com/aikorea/awesome-rl

We have pages for other topics: awesome-rnn, awesome-deep-vision, awesome-random-forest

Maintainers: Hyunsoo Kim, Jiwon Kim

We are looking for more contributors and maintainers!

Table of Contents

• Codes
• Theory
  • Lectures
  • Books
  • Surveys
  • Papers / Thesis
• Applications
  • Game Playing
  • Robotics
  • Control
  • Operations Research
  • Human Computer Interaction
• Tutorials / Websites
• Online Demos
• Open Source Reinforcement Learning Platforms

Codes

• Codes for examples and exercises in Richard Sutton and Andrew Barto's Reinforcement Learning: An Introduction
• Python Code
• MATLAB Code
• C/Lisp Code
• Book
• Simulation code for Reinforcement Learning Control Problems
• Pole-Cart Problem
• Q-learning Controller
• MATLAB Environment and GUI for Reinforcement Learning
• Reinforcement Learning Repository - University of Massachusetts, Amherst
• Brown-UMBC Reinforcement Learning and Planning Library (Java)
• Reinforcement Learning in R (MDP, Value Iteration)
• Reinforcement Learning Environment in Python and MATLAB
• RL-Glue (standard interface for RL) and RL-Glue Library
• PyBrain Library - Python-Based Reinforcement learning, Artificial intelligence, and Neural network
• RLPy Framework - Value-Function-Based Reinforcement Learning Framework for Education and Research
• Maja - Machine learning framework for problems in Reinforcement Learning in python
• TeachingBox - Java based Reinforcement Learning framework
• Policy Gradient Reinforcement Learning Toolbox for MATLAB
• PIQLE - Platform Implementing Q-Learning and other RL algorithms
• BeliefBox - Bayesian reinforcement learning library and toolkit
• Deep Q-Learning with Tensor Flow - A deep Q learning demonstration using Google Tensorflow
• Atari - Deep Q-networks and asynchronous agents in Torch
• AgentNet - A python library for deep reinforcement learning and custom recurrent networks using Theano+Lasagne.