Reinforcement Learning
Tutorials
Demystifying Deep Reinforcement Learning (Part1)
http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/
Deep Reinforcement Learning With Neon (Part2)
http://neuro.cs.ut.ee/deep-reinforcement-learning-with-neon/
Deep Reinforcement Learning (by David Silver, Google DeepMind)
- slides: http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf
- pan: http://pan.baidu.com/s/1qWBOJGo
Courses
David Silver Reinforcement Learning 2015
- author: Google DeepMind AlphaGo, David Silver
- video: http://pan.baidu.com/s/1bnWGuIz/
Papers
Playing Atari with Deep Reinforcement Learning(Google DeepMind)
- 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
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
- arXiv: http://arxiv.org/abs/1507.00814
- notes: https://www.evernote.com/shard/s189/sh/a4262b84-a322-4f77-9a76-569278be84af/b8c3e146a76ca3853f560bb03b60a481
Action-Conditional Video Prediction using Deep Networks in Atari Games
- homepage: https://sites.google.com/a/umich.edu/junhyuk-oh/action-conditional-video-prediction
- arxiv: http://arxiv.org/abs/1507.08750
- github: https://github.com/junhyukoh/nips2015-action-conditional-video-prediction
- video: http://video.weibo.com/show?fid=1034:98062f3d83e41da6faa99cde5aa1ac97
Continuous control with deep reinforcement learning(Google Deepmind)
Benchmarking for Bayesian Reinforcement Learning
- paper: http://arxiv.org/abs/1509.04064v1
- code: https://github.com/mcastron/BBRL/
- reading: http://blogs.ulg.ac.be/damien-ernst/benchmarking-for-bayesian-reinforcement-learning/
Giraffe: Using Deep Reinforcement Learning to Play Chess
Human-level control through deep reinforcement learning(Google DeepMind. 2015 Nature)
- paper: http://pan.baidu.com/s/1kTiwzOF
- code: https://sites.google.com/a/deepmind.com/dqn/
- github: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
- github: https://github.com/tambetm/simple_dqn
- discussion: 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
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning(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
MazeBase: A Sandbox for Learning from Games(New York University & Facebook AI Research)
Learning Simple Algorithms from Examples(New York University & Facebook AI Research)
Multiagent Cooperation and Competition with Deep Reinforcement Learning
- arXiv: http://arxiv.org/abs/1511.08779
- github: https://github.com/NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player
Active Object Localization with Deep Reinforcement Learning
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies
State of the Art Control of Atari Games Using Shallow Reinforcement Learning
Angrier Birds: Bayesian reinforcement learning
- arxiv: http://arxiv.org/abs/1601.01297
- github: https://github.com/imanolarrieta/angrybirds
- gitxiv: http://gitxiv.com/posts/Nr2N7j4YrR4gnCYK9/angrier-birds-bayesian-reinforcement-learning
Prioritized Experience Replay
Asynchronous Methods for Deep Reinforcement Learning
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Insights in Reinforcement Learning (MSc thesis)
Using Deep Q-Learning to Control Optimization Hyperparameters
Continuous Deep Q-Learning with Model-based Acceleration
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Projects
Using Deep Q-Network to Learn How To Play Flappy Bird
用Tensorflow基于Deep Q Learning DQN 玩Flappy Bird
- blog: http://blog.csdn.net/songrotek/article/details/50951537
- github: https://github.com/songrotek/DRL-FlappyBird
TorchQLearning
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
Blogs
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.
- blog: https://medium.com/@harvitronix/using-reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-6e782cc7d4c6#.p8ug6snri
- github: https://github.com/harvitronix/reinforcement-learning-car
Reinforcement learning in Python to teach a virtual car to avoid obstacles — part 2
Some Reinforcement Learning Algorithms in Python, C++
learning to do laps with reinforcement learning and neural nets
Get a taste of reinforcement learning — implement a tic tac toe agent
Best reinforcement learning libraries?
- reddit: https://www.reddit.com/r/MachineLearning/comments/4b2ugc/best_reinforcement_learning_libraries/
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#
Resources
Deep Reinforcement Learning Papers
https://github.com/junhyukoh/deep-reinforcement-learning-papers
Awesome Reinforcement Learning
- website: http://aikorea.org/awesome-rl/?utm_content=buffer5d0f3&utm_medium=social&utm_source=plus.google.com&utm_campaign=buffer#online-demos
- github: https://github.com/aikorea/awesome-rl
Deep Reinforcement Learning Papers
Reading and Questions
What are the best books about reinforcement learning?
https://www.quora.com/What-are-the-best-books-about-reinforcement-learning
