Control

• [Paper
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Game Playing

• [Paper
• [arXiv
• [arXiv
• [DOI - tw3IWgTseRnLpAc9xQq-vTA2Z5Ji9lg16_WvCy4SaOgpK5XXA6ecqo8d8J7l4EJsdjwai53GqKt-7JuioG0r3iV67MQIro74l6IxvmcVNKBgOwiMGi8U0izJStLpmQp6Vmi_8Lw_A%3D%3D) [[Code]](https://sites.google.com/a/deepmind.com/dqn/) [[Video]](https://www.youtube.com/watch?v=iqXKQf2BOSE)
• [Paper
• [DOI - 019-1724-z.epdf) [[Video]](https://deepmind.com/research/open-source/alphastar-resources)
• [arXiv
• [arXiv
• Flappy Bird Reinforcement Learning
• [Paper

Human Computer Interaction

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Operations Research

• [Paper
• [Paper
• [DOI - reinforcement-learning-for-semiconductor-production-scheduling.pdf)
• [DOI - reinforcement-learning-for-semiconductor-production-scheduling.pdf)
• [DOI - reinforcement-learning-for-semiconductor-production-scheduling.pdf)

Robotics

• [Paper
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• [ArXiv
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• [ArXiv - c17RKh3uE)] [[Code](https://github.com/resibots/cully_2015_nature)]
• [DOI
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• [Paper

Human Computer Interaction

• 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
• 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
• MATLAB Code
• POMDP for Dummies
• MATLAB Software, presentations, and demo videos

• MATLAB Code (BROKEN LINK)
• C/Lisp Code
• Book
• Python Code
• Julia Code
• Exercise Solutions
• C/Lisp Code
• Brown-UMBC Reinforcement Learning and Planning Library (Java)
• PyBrain Library - Python-Based Reinforcement learning, Artificial intelligence, and Neural network
• Maja - Machine learning framework for problems in Reinforcement Learning in python
• TeachingBox - Java based Reinforcement Learning framework
• Policy Gradient Reinforcement Learning Toolbox for MATLAB
• Deep Q-Learning with TensorFlow - 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.
• Reinforcement Learning Examples by RLCode - A Collection of minimal and clean reinforcement learning examples
• OpenAI Baselines - Well tested implementations ([and results](https://github.com/openai/baselines-results)) of reinforcement learning algorithms from OpenAI
• PyTorch Deep RL - Popular deep RL algorithm implementations with PyTorch
• ChainerRL - Popular deep RL algorithm implementations with Chainer
• Black-DROPS - Modular and generic code for the model-based policy search Black-DROPS algorithm (IROS 2017 paper) and easy integration with the [DART](http://dartsim.github.io/) simulator
• Gold - A reinforcement learning library for Golang.
• Jumanji - A Suite of Industry-Driven Hardware-Accelerated RL Environments written in JAX.
• Pole-Cart Problem
• Q-learning Controller
• BeliefBox - Bayesian reinforcement learning library and toolkit
• Maja - Machine learning framework for problems in Reinforcement Learning in python

Human Computer Interaction

• Deep Q-Learning Demo - A deep Q learning demonstration using ConvNetJS
• Reinforcement Learning Demo - A reinforcement learning demo using reinforcejs by Andrej Karpathy

Human Computer Interaction

• Microsoft AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research.
• OpenAI gym - A toolkit for developing and comparing reinforcement learning algorithms
• OpenAI universe - A software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications
• DeepMind Lab - A customisable 3D platform for agent-based AI research
• Project Malmo - A platform for Artificial Intelligence experimentation and research built on top of Minecraft by Microsoft
• Retro Learning Environment - An AI platform for reinforcement learning based on video game emulators. Currently supports SNES and Sega Genesis. Compatible with OpenAI gym.
• UETorch - A Torch plugin for Unreal Engine 4 by Facebook
• TorchCraft - Connecting Torch to StarCraft
• garage - A framework for reproducible reinformcement learning research, fully compatible with OpenAI Gym and DeepMind Control Suite (successor to rllab)
• TensorForce - Practical deep reinforcement learning on TensorFlow with Gitter support and OpenAI Gym/Universe/DeepMind Lab integration.
• OpenAI lab - An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
• keras-rl - State-of-the art deep reinforcement learning algorithms in Keras designed for compatibility with OpenAI.
• MAgent - A Platform for Many-agent Reinforcement Learning.
• SLM Lab - A research framework for Deep Reinforcement Learning using Unity, OpenAI Gym, PyTorch, Tensorflow.
• Unity ML Agents - Create reinforcement learning environments using the Unity Editor
• DI-engine - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
• Intel Coach - Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms.

Books

• [Book
• [Book - learning-an-introduction)
• [Book Chapter
• [Book (Amazon)
• [Book (Amazon)
• [Book(Manning)
• BOOK, VIDEOLECTURES, AND COURSE MATERIAL, 2019
• [Book
• BOOK, VIDEOLECTURES, AND COURSE MATERIAL, 2019
• [Book
• [Book (Amazon)
• [Book (Amazon)

Lectures

• UCL
• Lecture 8: Markov Decision Processes 1
• Lecture 9: Markov Decision Processes 2
• Lecture 10: Reinforcement Learning 1
• Lecture 11: Reinforcement Learning 2
• Stanford
• UC Berkeley
• CMU
• MIT
• Lecture 2: Deep Reinforcement Learning for Motion Planning
• Introduction to AI for video games
• Monte Carlo Prediction
• Q learning explained
• Solving the basic game of Pong
• Actor Critic Algorithms
• War Robots
• Mutual Information
• Reinforcement Learning: A Six Part Series
• The Bellman Equations, Dynamic Programming, and Generalized Policy Iteration
• Monte Carlo And Off-Policy Methods
• TD Learning, Sarsa, and Q-Learning
• UCL
• UCL
• Udacity (Georgia Tech.)
• Introduction to AI for video games
• Monte Carlo Prediction
• Q learning explained
• Solving the basic game of Pong
• Actor Critic Algorithms
• War Robots
• Mutual Information
• Reinforcement Learning: A Six Part Series
• The Bellman Equations, Dynamic Programming, and Generalized Policy Iteration
• Monte Carlo And Off-Policy Methods
• TD Learning, Sarsa, and Q-Learning
• Reinforcement Learning: A Six Part Series
• The Bellman Equations, Dynamic Programming, and Generalized Policy Iteration
• Monte Carlo And Off-Policy Methods
• TD Learning, Sarsa, and Q-Learning
• Stanford
• Lecture 2: Deep Reinforcement Learning for Motion Planning