Merge pull request #86 from philwinder/master

docs: rl book - reinforcement learning

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. [Supplementary 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)
 
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 ## 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)
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+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)
 
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 ## 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