aikorea / awesome-rl Goto Github PK

Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (2nd Edition, in progress, 2018) can now point to
http://incompleteideas.net/book/bookdraft2018mar11.pdf

instead of

http://incompleteideas.net/book/bookdraft2018jan1.pdf

cheers!

Hi,
I'm taking the course RL instructed by Sutton this term. However I find there is only matlab and lisp code available and both are out-of-date and incomplete. So I'm trying to replicate all the examples for the in-progress second edition of the book in python. I have done with all the examples from chapter 1 to chapter 6 and I will complete all the chapters by the end of this term (Dec 2016). My python code is here: https://github.com/ShangtongZhang/reinforcement-learning-an-introduction.
All codes are neat and well documented. Would you like to add my code to this awesome list?
Thanks

Hello, I have been reading the PRGLight source code recently. Can I communicate with you? This is my Wechat: Ljw_36

The publication year must be 2001 instead of 2011 (refer to line 166)

• Autonomous helicopter control using Reinforcement Learning Policy Search Methods (Bagnell, ICRA 2001)

Referenced paper | Google Scholar search

Hi,

There is a new environment for artificial agent interacting with Websites, and I think it should be added to the environment:
Mini World of Bits ("MiniWoB") is a benchmark for reinforcement learning agents who interact with websites. Here are the related resources that I found:

• MiniWoB++ demos: an extension of the OpenAI MiniWoB benchmark. It adds a number of tasks, Javascript interfaces, as well as Python code for interacting with the environment via Selenium.
• MiniWoB++ demos from users: Various data collected from Mechanical Turk
• World of Bits: An Open-Domain Platform for Web-Based Agents by Shi et al., 2017

Add
A Free course in Deep Reinforcement Learning from beginner to expert
https://simoninithomas.github.io/Deep_reinforcement_learning_Course/

"Short introduction to some Reinforcement Learning algorithms" link is broken.

Please replace with the link below.
https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf

Richard Sutton's google drive containing lectures, videos, code, additional reading material, exams, assignments etc. It's a very comprehensive resource.
Richard Sutton's public google drive

The latest draft of the RL book
Reinforcement Learning: An Introduction March 22, 2018

Implementation of different chapters for RL. Good for exercises and in-depth understanding of concepts
dennybritz GitHub page

An Introduction to Deep Reinforcement Learning
https://arxiv.org/pdf/1811.12560.pdf

BeliefBox is not a Bayesian RL library.
From Christos Dimitrakakis's website, it is a statistical inference and decision making toolbox.
I quickly skimmed through it and could not find any Bayesian RL method implemented (QLearning, BEETLE, GPTD, ...).

can you please update the links for awesome-rnn and other pages you have. Thanks for maintaining the curated list

Under Codes, bullet point Books link below is broken
http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html