From 0f4ad0ab283bcfb6d06f59459d4b4e1070c6125b Mon Sep 17 00:00:00 2001 From: Phil Winder Date: Tue, 25 May 2021 14:19:24 +0100 Subject: [PATCH 1/2] docs: rl book - reinforcement learning Shameless suggestion to add my book and associated links. :-) --- reinforcement-learning/README.md | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/reinforcement-learning/README.md b/reinforcement-learning/README.md index df83fae..7530f75 100644 --- a/reinforcement-learning/README.md +++ b/reinforcement-learning/README.md @@ -10,6 +10,7 @@ In these courses, you will learn the foundations of Reinforcement Learning. 2. [Reinforcement Learning - Stanford CS234](http://web.stanford.edu/class/cs234/index.html) 3. [Reinforcement Learning - IIT-M CS230](https://youtu.be/TIlDzLZPyhY) 4. [Excursions in Reinforcement Learning - Mila](http://pierrelucbacon.com/teaching/) +5. [Supplamentary Materials from Reinforcement Learning Book](https://rl-book.com/supplementary_materials/) ### Deep Reinforcement Learning @@ -25,30 +26,26 @@ In these courses, you will learn the foundations of Reinforcement Learning. 1. [Deep Multi-Task and Meta Learning](https://cs330.stanford.edu/) 2. [Trust Policy Optimisation series](http://www.depthfirstlearning.com/2018/TRPO) - - - ## Books 1. [Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition](http://incompleteideas.net/book/the-book-2nd.html) 2. [Markov Decision Processes: Discrete Stochastic Dynamic Programming by Martin Puterman](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316887) 3. [Reinforcement Learning and Optimal Control by Dimitri Bertsekas](https://web.mit.edu/dimitrib/www/RLbook.html) 4. [Grokking Deep Reinforcement Learining](https://www.manning.com/books/grokking-deep-reinforcement-learning) - - +5. [Reinforcement Learning: Industrial Applications of Intelligent Agents](https://rl-book.com) ## Clean Implementations 1. [RL-Adventure](https://github.com/higgsfield/RL-Adventure) and [RL-Adventure2](https://github.com/higgsfield/RL-Adventure) by [higgsfield](https://higgsfield.github.io/) 2. [RLlib: Scalable Reinforcement Learning](https://docs.ray.io/en/latest/rllib.html#rllib-scalable-reinforcement-learning) - ## Blog Posts/Tutorials 1. [RL— Introduction to Deep Reinforcement Learning](https://medium.com/@jonathan_hui/rl-introduction-to-deep-reinforcement-learning-35c25e04c199) 2. [Deep Reinforcement Series by Jonathan Hui](https://medium.com/@jonathan_hui/rl-deep-reinforcement-learning-series-833319a95530) 3. [All the fantastic blogs by Lilian Weng](https://lilianweng.github.io/lil-log/) 4. [Debugging RL, Without the Agonizing Pain by Andy Jones](https://andyljones.com/posts/rl-debugging.html) +5. [Variety of Introductory Blog Posts on RL](https://rl-book.com/learn/) ## Research Papers From 9fcd3357098c01832390e978f714142f544e8941 Mon Sep 17 00:00:00 2001 From: Raj Ghugare Date: Tue, 25 May 2021 19:01:15 +0530 Subject: [PATCH 2/2] Update README.md --- reinforcement-learning/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/reinforcement-learning/README.md b/reinforcement-learning/README.md index 7530f75..3781416 100644 --- a/reinforcement-learning/README.md +++ b/reinforcement-learning/README.md @@ -10,7 +10,7 @@ In these courses, you will learn the foundations of Reinforcement Learning. 2. [Reinforcement Learning - Stanford CS234](http://web.stanford.edu/class/cs234/index.html) 3. [Reinforcement Learning - IIT-M CS230](https://youtu.be/TIlDzLZPyhY) 4. [Excursions in Reinforcement Learning - Mila](http://pierrelucbacon.com/teaching/) -5. [Supplamentary Materials from Reinforcement Learning Book](https://rl-book.com/supplementary_materials/) +5. [Supplementary Materials from Reinforcement Learning Book](https://rl-book.com/supplementary_materials/) ### Deep Reinforcement Learning