https://github.com/machine-learning-tokyo/deep_reinforcement_learning
Resources, papers, tutorials
https://github.com/machine-learning-tokyo/deep_reinforcement_learning
deep-reinforcement-learning reinforcement-learning resources
Last synced: 7 months ago
JSON representation
Resources, papers, tutorials
- Host: GitHub
- URL: https://github.com/machine-learning-tokyo/deep_reinforcement_learning
- Owner: Machine-Learning-Tokyo
- Created: 2019-05-03T06:06:39.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-04-18T06:28:14.000Z (almost 6 years ago)
- Last Synced: 2025-02-22T03:31:45.464Z (12 months ago)
- Topics: deep-reinforcement-learning, reinforcement-learning, resources
- Size: 1.01 MB
- Stars: 124
- Watchers: 16
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Deep Reinforcement Learning
### Introduction to Reinforcement Learning with David Silver, DeepMind
Watch the lectures from DeepMind research lead David Silver's course on reinforcement learning, taught at University College London.
[[Video lectures]](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)
- Lecture 1: Introduction to Reinforcement Learning
- Lecture 2: Markov Decision Processes
- Lecture 3: Planning by Dynamic Programming
- Lecture 4: Model-Free Prediction
- Lecture 5: Model-Free Control
- Lecture 6: Value Function Approximation
- Lecture 7: Policy Gradient Methods
- Lecture 8: Integrating Learning and Planning
- Lecture 9: Exploration and Exploitation
- Lecture 10: Case Study: RL in Classic Games
### Deep Reinforcement Learning: A Brief Survey
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
- [[Paper]](https://arxiv.org/abs/1708.05866)
- [IEEE Signal Processing Magazine | November 2017](https://ieeexplore.ieee.org/document/8103164)
[
](https://arxiv.org/abs/1708.05866)
### Spinning Up in Deep RL
Educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). It includes the following resources:
* a short [introduction](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html) to RL terminology, kinds of algorithms, and basic theory,
* an [essay](https://spinningup.openai.com/en/latest/spinningup/spinningup.html) about how to grow into an RL research role,
* a [curated list](https://spinningup.openai.com/en/latest/spinningup/keypapers.html) of important papers organized by topic,
* a well-documented [code repo](https://github.com/openai/spinningup) of short, standalone implementations of key algorithms,
* and a few [exercises](https://spinningup.openai.com/en/latest/spinningup/exercises.html) to serve as warm-ups.
[[Webpage]](https://spinningup.openai.com)
### Stanford CS234: Reinforcement Learning
Lecture Series. Stanford CS234: Reinforcement Learning (Winter 2019) - with Prof. Emma Brunskill
[[YouTube]](https://www.youtube.com/watch?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u&v=FgzM3zpZ55o)
### An Introduction to Deep Reinforcement Learning (2018)
Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau
[[PDF Book manuscript, Nov 2018]](https://arxiv.org/abs/1811.12560)
### CS294-112 Deep Reinforcement Learning
Lecture Series. UC Berkeley. Fall 2018.
Instructor : Sergey Levine
[Webpage](http://rail.eecs.berkeley.edu/deeprlcourse/)
[Youtube](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37)
### CS885 Reinforcement Learning
Lecture Series. University of Waterloo. Spring 2018
Instructor: Pascal Poupart
[Webpage](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/)
[Youtube](https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc)
### Advanced Deep Learning & Reinforcement Learning
Deepmind 2018.
[Youtube](https://www.youtube.com/watch?v=iOh7QUZGyiU&list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)
### RLSS 2018
Toronto 2018.
[Videos](http://videolectures.net/DLRLsummerschool2018_toronto/)
### RLSS 2017
Montreal 2017.
[Videos](http://videolectures.net/deeplearning2017_montreal/)
### Deep RL Bootcamp
Berkeley CA. Aug 2017
[Slides & Videos](https://sites.google.com/view/deep-rl-bootcamp/lectures)
### Introduction to Reinforcement Learning
DeepMind, 2015
Instructor : David Silver
[Youtube](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)
### Deep RL Bootcamp, Berkeley (2017)
By Pieter Abbeel, Chelsea Finn, Peter Chen, Andrej Karpathy et al.
[[Webpage]](https://sites.google.com/view/deep-rl-bootcamp/lectures)
### Reinforcement Learning Book
Written by [Richard Sutton](http://incompleteideas.net/index.html) and [Andrew Barto](http://www-anw.cs.umass.edu/~barto/).
[[Webpage]](http://incompleteideas.net/book/the-book-2nd.html) [[PDF]](http://incompleteideas.net/book/RLbook2018.pdf) [[Goodreads]](https://www.goodreads.com/book/show/39813875-reinforcement-learning)
### Denny Britz: Reinforcement Learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course. All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.
[[GitHub]](https://github.com/dennybritz/reinforcement-learning)