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Reinforcement Learning
Summary
Reinforcement learning has in recent years seen a lot of progress, to summarize all
of that in a single chapter is not possible. However, in this chapter we focused on the
recent successful RL algorithms. The chapter started by introducing the important
concepts in the RL field, its challenges, and the solutions to move forward. Next, we
delved into two important RL algorithms: the DQN and DDPG algorithms. Toward
the end of this chapter the book covered the important topics in the field of deep
learning. In the next chapter, we will move into applying what we have learned to
production.
References
1. https://www.technologyreview.com/s/614325/open-ai-algorithmslearned-tool-use-and-cooperation-after-hide-and-seekgames/?fbclid=IwAR1JvW-JTWnzP54bk9eCEvuJOq1y7vU4qz4OFfilWr7xHGH
sILakKSD9UjY
2. Coggan, Melanie. Exploration and Exploitation in Reinforcement Learning.
Research supervised by Prof. Doina Precup, CRA-W DMP Project at McGill
University (2004).
3. Lin, Long-Ji. Reinforcement learning for robots using neural networks. No. CMU-
CS-93-103. Carnegie-Mellon University Pittsburgh PA School of Computer
Science, 1993.
4. Schaul, Tom, John Quan, Ioannis Antonoglou, and David Silver. Prioritized
Experience Replay. arXiv preprint arXiv:1511.05952 (2015).
5. Chapter 4, Reinforcement Learning, Richard Sutton and Andrew Barto,
MIT Press. https://web.stanford.edu/class/psych209/Readings/
SuttonBartoIPRLBook2ndEd.pdf.
6. Dabney, Will, Mark Rowland, Marc G. Bellemare, and Rémi Munos.
Distributional Reinforcement Learning with Quantile Regression. In Thirty-
Second AAAI Conference on Artificial Intelligence. 2018.
7. Hessel, Matteo, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg
Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and
David Silver. Rainbow: Combining improvements in Deep Reinforcement Learning.
In Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
8. Pittsburgh PA School of Computer Science, 1993.
9. The details about different environments can be obtained from https://
gym. openai.com/envs.
10. Wiki pages are maintained for some environments at https://github.com/
openai/gym/wiki.
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