Reinforcement Learning

intro: A small collection of code snippets and notes explaining the foundations of the REINFORCE algorithm.
github: https://github.com/mathias-madsen/reinforce_tutorial

Deep Q-Learning Recap

http://blog.davidqiu.com/Research/%5B%20Recap%20%5D%20Deep%20Q-Learning%20Recap/

Introduction to Reinforcement Learning

intro: Joelle Pineau [McGill University]
video: http://videolectures.net/deeplearning2016_pineau_reinforcement_learning/
slides: http://videolectures.net/site/normal_dl/tag=1051677/deeplearning2016_pineau_reinforcement_learning_01.pdf

Courses

Advanced Topics: RL

UCL Course on RL

instructors: David Silver (Google DeepMind, AlphaGo)
homepage: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
youtube: https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
video: http://pan.baidu.com/s/1bnWGuIz/
assignment: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/Easy21-Johannes.pdf

CS 294: Deep Reinforcement Learning, Fall 2017

instructor: Sergey Levine
homepage: http://rll.berkeley.edu/deeprlcourse/
youtube: https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3
bilibili: https://www.bilibili.com/video/av21501169/

CS 294: Deep Reinforcement Learning, Spring 2017

course page: http://rll.berkeley.edu/deeprlcoursesp17/
github: https://github.com//txizzle/drl

Berkeley CS 294: Deep Reinforcement Learning

instructors: John Schulman, Pieter Abbeel
homepage: http://rll.berkeley.edu/deeprlcourse/
youtube: https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX
mirror: https://pan.baidu.com/s/1hsQcm1Y

(Udacity) Reinforcement Learning - Offered at Georgia Tech as CS 8803

instructor: Charles Isbell, Michael Littman
homepage: https://www.udacity.com/course/reinforcement-learning–ud600
homepage: https://classroom.udacity.com/courses/ud820/lessons/684808907/concepts/6512308530923

CS229 Lecture notes Part XIII: Reinforcement Learning and Control

intro: Andrew Ng
lecture notes: http://cs229.stanford.edu/notes/cs229-notes12.pdf

Practical_RL: A course in reinforcement learning in the wild

github: https://github.com/yandexdataschool/Practical_RL

Reinforcement Learning (COMP-762) Winter 2017

course page: http://www.cs.mcgill.ca/~dprecup/courses/rl.html
lectures: http://www.cs.mcgill.ca/~dprecup/courses/RL/lectures.html

**Deep RL Bootcamp - 26-27 August 2017

Berkeley CA**

lectures: https://sites.google.com/view/deep-rl-bootcamp/lectures
video: https://www.bilibili.com/video/av15568836/

CMPUT 366: Intelligent Systems and CMPUT 609: Reinforcement Learning & Artificial Intelligence

intro: by Rich Sutton, Adam White
lecture video: https://drive.google.com/drive/folders/0B3w765rOKuKAMG9lbmRacFdsLWM?direction=a

Deep Reinforcement Learning and Control (Spring 2017, CMU 10703)

instructors: Katerina Fragkiadaki, Ruslan Satakhutdinov
homepage: https://katefvision.github.io/
video: https://www.youtube.com/playlist?list=PLpIxOj-HnDsNPFdu2UqCu2McJKHs-eWXv
mirror: https://www.bilibili.com/video/av18865689/

Advanced Deep Learning & Reinforcement Learning

intro: DeepMind
youtube: https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
bilibili: https://www.bilibili.com/video/av36621866/
github: https://github.com/RylanSchaeffer/ucl-adv-dl-rl

Papers

Playing Atari with Deep Reinforcement Learning

intro: Google DeepMind. NIPS Deep Learning Workshop 2013
arxiv: http://arxiv.org/abs/1312.5602
github: https://github.com/kristjankorjus/Replicating-DeepMind
demo: http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html
github: https://github.com/Kaixhin/Atari
github(Tensorflow): https://github.com/gliese581gg/DQN_tensorflow
summary: https://github.com/aleju/papers/blob/master/neural-nets/Playing_Atari_with_Deep_Reinforcement_Learning.md

Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

intro: NIPS 2014
keywords: DQN, MCTS
paper: http://papers.nips.cc/paper/5421-scalable-inference-for-neuronal-connectivity-from-calcium-imaging
paper: https://web.eecs.umich.edu/~baveja/Papers/UCTtoCNNsAtariGames-FinalVersion.pdf

Replicating the Paper “Playing Atari with Deep Reinforcement Learning”

intro: University of Tartu
technical report: https://courses.cs.ut.ee/MTAT.03.291/2014_spring/uploads/Main/Replicating%20DeepMind.pdf

A Tutorial for Reinforcement Learning

paper: http://web.mst.edu/~gosavia/tutorial.pdf
code(C): http://web.mst.edu/~gosavia/bookcodes.html
code(Matlab): http://web.mst.edu/~gosavia/mrrl_website.html

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Massively Parallel Methods for Deep Reinforcement Learning

intro: ICML 2015. DeepMind
keywords: DQN, Gorila
arxiv: https://arxiv.org/abs/1507.04296

Action-Conditional Video Prediction using Deep Networks in Atari Games

Deep Recurrent Q-Learning for Partially Observable MDPs

intro: AAAI 2015
arxiv: https://arxiv.org/abs/1507.06527

Continuous control with deep reinforcement learning

intro: Google DeepMind
arxiv: http://arxiv.org/abs/1509.02971
github: https://github.com/iassael/torch-policy-gradient
github: https://github.com/stevenpjg/ddpg-aigym
github(TensorFlow + OpenAI Gym): https://github.com/SimonRamstedt/ddpg

Benchmarking for Bayesian Reinforcement Learning

Deep Reinforcement Learning with Double Q-learning

intro: AAAI 2016
arxiv: https://arxiv.org/abs/1509.06461

Giraffe: Using Deep Reinforcement Learning to Play Chess

arxiv: http://arxiv.org/abs/1509.01549

Human-level control through deep reinforcement learning

intro: Google DeepMind. 2015 Nature
paper: http://www.readcube.com/articles/10.1038/nature14236?shared_access_token=Lo_2hFdW4MuqEcF3CVBZm9RgN0jAjWel9jnR3ZoTv0P5kedCCNjz3FJ2FhQCgXkApOr3ZSsJAldp-tw3IWgTseRnLpAc9xQq-vTA2Z5Ji9lg16_WvCy4SaOgpK5XXA6ecqo8d8J7l4EJsdjwai53GqKt-7JuioG0r3iV67MQIro74l6IxvmcVNKBgOwiMGi8U0izJStLpmQp6Vmi_8Lw_A%3D%3D
paper: http://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf
github(Lua/Torch): https://github.com/deepmind/dqn
mirror: http://pan.baidu.com/s/1kTiwzOF
code: https://sites.google.com/a/deepmind.com/dqn/
youtube: https://www.youtube.com/watch?v=V2wzkPmiB_A
github: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
github: https://github.com/tambetm/simple_dqn
github: https://github.com/devsisters/DQN-tensorflow
reddit: https://www.reddit.com/r/MachineLearning/comments/2x4yy1/google_deepmind_nature_paper_humanlevel_control

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

arxiv: http://arxiv.org/abs/1510.02173

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

intro: Google DeepMind
arxiv: http://arxiv.org/abs/1509.08731
notes: https://www.evernote.com/shard/s189/sh/8c7ff9d9-c321-4e83-a802-58f55ebed9ac/bfc614113180a5f4624390df56e73889

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

intro: ICLR 2016
arxiv: http://arxiv.org/abs/1511.06342
github: https://github.com/eparisotto/ActorMimic

MazeBase: A Sandbox for Learning from Games

intro: New York University & Facebook AI Research
arxiv: http://arxiv.org/abs/1511.07401

Learning Simple Algorithms from Examples

intro: New York University & Facebook AI Research
arxiv: http://arxiv.org/abs/1511.07275
github: https://github.com/wojzaremba/algorithm-learning

Learning Algorithms from Data

PhD thesis: http://www.cs.nyu.edu/media/publications/zaremba_wojciech.pdf
github: https://github.com/wojzaremba/algorithm-learning

Multiagent Cooperation and Competition with Deep Reinforcement Learning

Active Object Localization with Deep Reinforcement Learning

arxiv: http://arxiv.org/abs/1511.06015

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions

arxiv: http://arxiv.org/abs/1512.01124

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies

arxiv: http://arxiv.org/abs/1512.02011

State of the Art Control of Atari Games Using Shallow Reinforcement Learning

arxiv: http://arxiv.org/abs/1512.01563

Angrier Birds: Bayesian reinforcement learning

Prioritized Experience Replay

arxiv: http://arxiv.org/abs/1511.05952

Dueling Network Architectures for Deep Reinforcement Learning

intro: ICML 2016 best paper
arxiv: http://arxiv.org/abs/1511.06581
notes: https://hadovanhasselt.wordpress.com/2016/06/20/best-paper-at-icml-dueling-network-architectures-for-deep-reinforcement-learning/

Asynchronous Methods for Deep Reinforcement Learning

arxiv: http://arxiv.org/abs/1602.01783
github(Tensorflow): https://github.com/traai/async-deep-rl
github(Tensorflow+Keras+OpenAI Gym): https://github.com/coreylynch/async-rl
github(Tensorflow): https://github.com/devsisters/async-rl-tensorflow
github(PyTorch): https://github.com/ikostrikov/pytorch-a3c
notes: https://blog.acolyer.org/2016/10/10/asynchronous-methods-for-deep-reinforcement-learning/

Graying the black box: Understanding DQNs

arxiv: http://arxiv.org/abs/1602.02658

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

arxiv: http://arxiv.org/abs/1602.02672

Value Iteration Networks

intro: NIPS 2016, Best Paper Award. University of California, Berkeley
arxiv: http://arxiv.org/abs/1602.02867
github(official, Theano): https://github.com/avivt/VIN
github: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks
github: https://github.com/onlytailei/PyTorch-value-iteration-networks
github: https://github.com/kentsommer/pytorch-value-iteration-networks
github: https://github.com/neka-nat/vin-keras
notes(by Andrej Karpathy): https://github.com/karpathy/paper-notes/blob/master/vin.md

Insights in Reinforcement Learning

intro: MSc thesis
mirror: http://pan.baidu.com/s/1bn51BYJ

Using Deep Q-Learning to Control Optimization Hyperparameters

arxiv: http://arxiv.org/abs/1602.04062

Continuous Deep Q-Learning with Model-based Acceleration

arxiv: http://arxiv.org/abs/1603.00748

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

arxiv: http://arxiv.org/abs/1603.01121

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

intro: MIT
arxiv: https://arxiv.org/abs/1604.06057
github: https://github.com/EthanMacdonald/h-DQN

Benchmarking Deep Reinforcement Learning for Continuous Control

arxiv: http://arxiv.org/abs/1604.06778
github: https://github.com/rllab/rllab
doc: https://rllab.readthedocs.org/en/latest/

Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning

Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks

arxiv: http://arxiv.org/abs/1605.05359

Deep Successor Reinforcement Learning (MIT)

arxiv: http://arxiv.org/abs/1606.02396
github: https://github.com/Ardavans/DSR

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

arxiv: https://arxiv.org/abs/1605.06676
github: https://github.com/iassael/learning-to-communicate

Deep Reinforcement Learning with Regularized Convolutional Neural Fitted Q Iteration RC-NFQ: Regularized Convolutional Neural Fitted Q Iteration

intro: A batch algorithm for deep reinforcement learning. Incorporates dropout regularization and convolutional neural networks with a separate target Q network.
paper: http://machineintelligence.org/papers/rc-nfq.pdf
github: https://github.com/cosmoharrigan/rc-nfq

Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

intro: Facebook AI Research
arxiv: http://arxiv.org/abs/1609.02993

Bayesian Reinforcement Learning: A Survey

arxiv: http://arxiv.org/abs/1609.04436

Playing FPS Games with Deep Reinforcement Learning

Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States

intro: University of Washington & UC Berkeley
arxiv: https://arxiv.org/abs/1610.01112

Utilization of Deep Reinforcement Learning for saccadic-based object visual search

arxiv: https://arxiv.org/abs/1610.06492

Learning to Navigate in Complex Environments

intro: Google DeepMind
arxiv: https://arxiv.org/abs/1611.03673
github: https://github.com/deepmind/lab
youtube: https://www.youtube.com/watch?v=lNoaTyMZsWI

Reinforcement Learning with Unsupervised Auxiliary Tasks

intro: DeepMind. ICLR 2017 oral
arxiv: https://arxiv.org/abs/1611.05397

Learning to reinforcement learn

intro: DeepMind
arxiv: https://arxiv.org/abs/1611.05763

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

intro: Graduate Training Center of Neuroscience & MSR
arxiv: https://arxiv.org/abs/1611.07078

Exploration for Multi-task Reinforcement Learning with Deep Generative Models

intro: NIPS Deep Reinforcement Learning Workshop 2016
arxiv: https://arxiv.org/abs/1611.09894

Neural Combinatorial Optimization with Reinforcement Learning

intro: Google Brain
keywords: traveling salesman problem (TSP)
arxiv: https://arxiv.org/abs/1611.09940

Loss is its own Reward: Self-Supervision for Reinforcement Learning

arxiv: https://arxiv.org/abs/1612.07307

Reinforcement Learning Using Quantum Boltzmann Machines

intro: 1QB Information Technologies (1QBit)
arxiv: https://arxiv.org/abs/1612.05695

Deep Reinforcement Learning applied to the game Bubble Shooter

bachelor thesis: https://staff.fnwi.uva.nl/b.bredeweg/pdf/BSc/20152016/Samson.pdf
github: https://github.com/laurenssam/AlphaBubble
demo: https://www.youtube.com/watch?v=DPAKFenNgbs

Deep Reinforcement Learning: An Overview

arxiv: https://arxiv.org/abs/1701.07274

Robust Adversarial Reinforcement Learning

intro: CMU & Google Brain & Google Research
arxiv: https://arxiv.org/abs/1703.02702

Beating Atari with Natural Language Guided Reinforcement Learning

intro: Stanford University
arxiv: https://arxiv.org/abs/1704.05539

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

intro: Imperial College London
arxiv: https://arxiv.org/abs/1705.06769
github: https://github.com/Nat-D/FeatureControlHRL

Distral: Robust Multitask Reinforcement Learning

intro: DeepMind
keywords: Distill, transfer learning
arxiv: https://arxiv.org/abs/1707.04175

Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations

intro: Syracuse University & University of California, Riverside
arxiv: https://arxiv.org/abs/1710.03792

Robust Deep Reinforcement Learning with Adversarial Attacks

https://arxiv.org/abs/1712.03632

Variational Deep Q Network

intro: Second workshop on Bayesian Deep Learning (NIPS 2017). Columbia University
arxiv: https://arxiv.org/abs/1711.11225

On Monte Carlo Tree Search and Reinforcement Learning

https://www.jair.org/media/5507/live-5507-10333-jair.pdf

Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes

intro: deepsense.ai & Intel & Polish Academy of Sciences
arxiv: https://arxiv.org/abs/1801.02852
gihtub: https://github.com//anonymous-author1/DDRL

GAN Q-learning

https://arxiv.org/abs/1805.04874

Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents

intro: Visual Geometry Group, University of Oxford & Element AI & Polytechnique Montreal, Mila & Canada CIFAR AI Chair
arxiv: https://arxiv.org/abs/1904.01318

Surveys

Reinforcement Learning: A Survey

intro: JAIR 1996
project page: http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/rl-survey.html
arxiv: http://arxiv.org/abs/cs/9605103

A Brief Survey of Deep Reinforcement Learning

intro: IEEE Signal Processing Magazine, Special Issue on Deep Learning for Image Understanding
intro: Imperial College London & Arizona State University
arxiv: https://arxiv.org/abs/1708.05866

Playing Doom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

arxiv: http://arxiv.org/abs/1605.02097
github: https://github.com/Marqt/ViZDoom
homepage: http://vizdoom.cs.put.edu.pl/
tutorial: http://vizdoom.cs.put.edu.pl/tutorial

Deep Reinforcement Learning From Raw Pixels in Doom

intro: Bachelor’s thesis
arxiv: https://arxiv.org/abs/1610.02164

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

intro: University of Oxford
arxiv: https://arxiv.org/abs/1612.00380

Reinforcement Learning via Recurrent Convolutional Neural Networks

intro: ICPR 2016
arxiv: https://arxiv.org/abs/1701.02392
github: https://github.com/tanmayshankar/RCNN_MDP

Shallow Updates for Deep Reinforcement Learning

intro: The Technion & UC Berkeley
arxiv: https://arxiv.org/abs/1705.07461
github(Official): https://github.com/Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL

Projects

TorchQLearning

github: https://github.com/SeanNaren/TorchQLearningExample

General_Deep_Q_RL: General deep Q learning framework

github: https://github.com/VinF/General_Deep_Q_RL
wiki: https://github.com/VinF/General_Deep_Q_RL/wiki

Snake: Toy example of deep reinforcement model playing the game of snake

github: https://github.com/bitwise-ben/Snake

Using Deep Q Networks to Learn Video Game Strategies

github: https://github.com/asrivat1/DeepLearningVideoGames

qlearning4k: Q-learning for Keras

intro: “Qlearning4k is a reinforcement learning add-on for the python deep learning library Keras. Its simple, and is ideal for rapid prototyping.”
github: https://github.com/farizrahman4u/qlearning4k

rlenvs: Reinforcement learning environments for Torch7, inspired by RL-Glue

github: https://github.com/Kaixhin/rlenvs

deep_rl_ale: An implementation of Deep Reinforcement Learning / Deep Q-Networks for Atari games in TensorFlow

github: https://github.com/Jabberwockyll/deep_rl_ale

Chimp: General purpose framework for deep reinforcement learning

github: https://github.com/sisl/Chimp

Deep Q Learning for ATARI using Tensorflow

github: https://github.com/mrkulk/deepQN_tensorflow

DeepQLearning: A powerful machine learning algorithm utilizing Q-Learning and Neural Networks, implemented using Torch and Lua.

github: https://github.com/blakeMilner/DeepQLearning

OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms

homepage: https://gym.openai.com/
github: https://github.com/openai/gym

DeeR: DEEp Reinforcement learning framework

github: https://github.com/VinF/deer/
docs: http://deer.readthedocs.io/en/latest/

KeRLym: A Deep Reinforcement Learning Toolbox in Keras

Pack of Drones: Layered reinforcement learning for complex behaviors

github: https://github.com/MickyDowns/deep-theano-rnn-lstm-car
youtube: https://www.youtube.com/watch?v=WrLRGzbfeZc

RL Helicopter Game: Q-Learning and DQN Reinforcement Learning to play the Helicopter Game - Keras based!

project page: http://dandxy89.github.io/rf_helicopter/
github: https://github.com/dandxy89/rf_helicopter

Playing Mario with Deep Reinforcement Learning

github: https://github.com/aleju/mario-ai

Deep Attention Recurrent Q-Network

intro: Deep Reinforcement Learning Workshop, NIPS 2015. DeepHack Game
arxiv: https://arxiv.org/abs/1512.01693
github: https://github.com/5vision/DARQN

Deep Reinforcement Learning in TensorFlow

intro: TensorFlow implementation of Deep Reinforcement Learning papers
github: https://github.com/carpedm20/deep-rl-tensorflow

rltorch: A RL package for Torch that can also be used with openai gym

github: https://github.com/ludc/rltorch

deep_q_rl: Theano-based implementation of Deep Q-learning

github: https://github.com/spragunr/deep_q_rl

Reinforcement-trading

intro: This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.
github: https://github.com/deependersingla/deep_trader

dist-dqn：Distributed Reinforcement Learning using Deep Q-Network in TensorFlow

github: https://github.com/viswanathgs/dist-dqn

Deep Reinforcement Learning for Keras

github: https://github.com/matthiasplappert/keras-rl

RL4J: Reinforcement Learning for the JVM

intro: Reinforcement learning framework integrated with deeplearning4j.
github: https://github.com/deeplearning4j/rl4j

Teaching Your Computer To Play Super Mario Bros. – A Fork of the Google DeepMind Atari Machine Learning Project

dprl: Deep reinforcement learning package for torch7

github: https://github.com/PoHsunSu/dprl

Reinforcement Learning for Torch: Introducing torch-twrl

Alpha Toe - Using Deep learning to master Tic-Tac-Toe - Daniel Slater

blog: http://www.danielslater.net/2016/10/alphatoe.html
youtube: https://www.youtube.com/watch?v=Meb5hApAnj4
github: https://github.com/DanielSlater/AlphaToe

Tensorflow-Reinforce: Implementation of Reinforcement Learning Models in Tensorflow

github: https://github.com/yukezhu/tensorflow-reinforce

deep RL hacking on minecraft with malmo

github: https://github.com/matpalm/malmomo

ReinforcementLearning

intro: MC control, Q-learning, SARSA, Cross Entropy Method
github: https://github.com/janivanecky/ReinforcementLearning

markovjs: Reinforcement Learning in JavaScript

github: https://github.com/lsunsi/markovjs

Deep Q: Deep reinforcement learning with TensorFlow

github: https://github.com/tobegit3hub/deep_q

Deep Q-Learning Network in pytorch

https://github.com/transedward/pytorch-dqn

Tensorflow-RL: Implementations of deep RL papers and random experimentation

https://github.com/steveKapturowski/tensorflow-rl

Minimal and Clean Reinforcement Learning Examples

https://github.com/rlcode/reinforcement-learning

DeepRL: Highly modularized implementation of popular deep RL algorithms by PyTorch

https://github.com/ShangtongZhang/DeepRL

Self-Driving-Car-AI

intro: A simple self-driving car AI python script using the deep Q-learning algorithm
github: https://github.com//JianyangZhang/Self-Driving-Car-AI

Autonomous vehicle navigation based on Deep Reinforcement Learning

https://github.com//kaihuchen/DRL-AutonomousVehicles

Car Racing using Reinforcement Learning

intro: Stanford University
paper: https://web.stanford.edu/class/cs221/2017/restricted/p-final/elibol/final.pdf

Play Flappy Bird

Using Deep Q-Network to Learn How To Play Flappy Bird

github: https://github.com/yenchenlin/DeepLearningFlappyBird

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN)

github: https://github.com/li-haoran/DRL-FlappyBird

MXNET-Scala Playing Flappy Bird Using Deep Reinforcement Learning

github: https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/DRLFlappyBird

Flappy Bird Bot using Reinforcement Learning in Python

github: https://github.com/chncyhn/flappybird-qlearning-bot

Using Keras and Deep Q-Network to Play FlappyBird

Pong

Building a Pong playing AI in just 1 hour(plus 4 days training…)

sildes: https://speakerdeck.com/danielslater/building-a-pong-ai
github: https://github.com/DanielSlater/PyDataLondon2016
youtube: https://www.youtube.com/watch?v=n8NdT_3y9oY

Pong Neural Network(LIVE)

youtube: https://www.youtube.com/watch?v=Hqf__FlRlzg
github: https://github.com/llSourcell/pong_neural_network_live

Tips and Tricks

DeepRLHacks

intro: The Nuts and Bolts of Deep RL Research
github: https://github.com/williamFalcon/DeepRLHacks

Library

BURLAP: Brown-UMBC Reinforcement Learning and Planning (BURLAP) java code library

intro: for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them
homepage: http://burlap.cs.brown.edu/

AgentNet: Deep Reinforcement Learning library for humans

intro: A lightweight library to build and train deep reinforcement learning and custom recurrent networks using Theano+Lasagne
github: https://github.com/yandexdataschool/AgentNet

Atari Multitask & Transfer Learning Benchmark (AMTLB)

intro: Atari gauntlet for RL agents
project page: http://ai-on.org/projects/multitask-and-transfer-learning.html
github: https://github.com/deontologician/atari_multitask

Coach: a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms

intro: Reinforcement Learning Coach by Intel® Nervana™ enables easy experimentation with state of the art Reinforcement Learning algorithms
homepage: http://coach.nervanasys.com/
github: https://github.com/NervanaSystems/coach

Blogs

Reinforcement learning’s foundational flaw

https://thegradient.pub/why-rl-is-flawed/

A Short Introduction To Some Reinforcement Learning Algorithms

http://webdocs.cs.ualberta.ca/~vanhasse/rl_algs/rl_algs.html

A Painless Q-Learning Tutorial

http://mnemstudio.org/path-finding-q-learning-tutorial.htm

Reinforcement Learning - Part 1

http://outlace.com/Reinforcement-Learning-Part-1/

Reinforcement Learning - Monte Carlo Methods

http://outlace.com/Reinforcement-Learning-Part-2/

Q-learning with Neural Networks

http://outlace.com/Reinforcement-Learning-Part-3/

Guest Post (Part I): Demystifying Deep Reinforcement Learning

http://www.nervanasys.com/demystifying-deep-reinforcement-learning/

Using reinforcement learning in Python to teach a virtual car to avoid obstacles: An experiment in Q-learning, neural networks and Pygame.

Reinforcement learning in Python to teach a virtual car to avoid obstacles — part 2

https://medium.com/@harvitronix/reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-part-2-93e614fcd238#.i0o643m1h

Some Reinforcement Learning Algorithms in Python, C++

pan: http://pan.baidu.com/s/1mhcYf3M#path=%252FImplementations%2520of%2520Some%2520Reinforcement%2520Learning%2520Algorithms

learning to do laps with reinforcement learning and neural nets

blog: http://matpalm.com/blog/drivebot/
github: https://github.com/matpalm/drivebot

Get a taste of reinforcement learning — implement a tic tac toe agent

https://medium.com/@shiyan/get-a-taste-of-reinforcement-learning-implement-a-tic-tac-toe-agent-deda5617b2e4#.59bx71a2h

Best reinforcement learning libraries?

reddit: https://www.reddit.com/r/MachineLearning/comments/4b2ugc/best_reinforcement_learning_libraries/

Super Simple Reinforcement Learning Tutorial

Reinforcement Learning in Python

github: https://github.com/NathanEpstein/pydata-reinforce

The Skynet Salesman

keyworkds: traveling salesman problem (TSP), deep Q learning
blog: http://multithreaded.stitchfix.com/blog/2016/07/21/skynet-salesman/
github: https://github.com/jn2clark/ReinforcementLearning/tree/master/DeepQ

Apprenticeship learning using Inverse Reinforcement Learning

Reinforcement Learning and DQN, learning to play from pixels

blog: https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html

Deep Learning in a Nutshell: Reinforcement Learning

https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-reinforcement-learning/

Write an AI to win at Pong from scratch with Reinforcement Learning

https://medium.com/@dhruvp/how-to-write-a-neural-network-to-play-pong-from-scratch-956b57d4f6e0#.n1pgn9chr

Learning Reinforcement Learning (with Code, Exercises and Solutions)

Deep Reinforcement Learning: Playing a Racing Game

https://lopespm.github.io/machine_learning/2016/10/06/deep-reinforcement-learning-racing-game.html

Experimenting with Reinforcement Learning and Active Inference

blog: http://www.araya.org/archives/955
github: https://github.com/arayabrain/BinarySearchLSTM

Deep reinforcement learning, battleship

blog: http://efavdb.com/battleship/
github: https://github.com/EFavDB/battleship

Deep Learning Research Review Week 2: Reinforcement Learning

https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning

Reinforcement Learning: Artificial Intelligence in Game Playing

https://medium.com/@pavelkordik/reinforcement-learning-the-hardest-part-of-machine-learning-b667a22995ca#.jjiitflok

Artificial Intelligence’s Next Big Step: Reinforcement Learning

http://thenewstack.io/reinforcement-learning-ready-real-world/

Let’s make a DQN

Let’s make a DQN

Theory: https://jaromiru.com/2016/09/27/lets-make-a-dqn-theory/
Implementation: https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/
Debugging: https://jaromiru.com/2016/10/12/lets-make-a-dqn-debugging/
Full DQN: https://jaromiru.com/2016/10/21/lets-make-a-dqn-full-dqn/
github: https://github.com/jaara/AI-blog/blob/master/CartPole-basic.py

Books

Reinforcement Learning: State-of-the-Art

intro: “The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.”
book: http://www.springer.com/gp/book/9783642276446#

Reinforcement Learning: An Introduction