Abstract
In this chapter we will study dynamic programming. Starting with the fundamental equation of dynamic programming as defined by Bellman, we will further dive deep into its generalization. We will understand the class of problems that can be solved with the framework of dynamic programming. Then we will study reinforcement learning as one subcategory of dynamic programming in detail. We will study the concepts of exploration and exploitation and the optimal tradeoff between them to achieve the best performance. We will also look at some variation of the reinforcement learning in the form of Q-learning and SARSA.
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References
Wikipedia - Dynamic Programming Applications https://en.wikipedia.org/wiki/Dynamic_programming#Algorithms_that_use_dynamic_programming
Shannon number https://en.wikipedia.org/wiki/Shannon_number
Deep Blue (chess computer) https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)
Setting up Mario Bros. in OpenAI’s gym https://becominghuman.ai/getting-mario-back-into-the-gym-setting-up-super-mario-bros-in-openais-gym-8e39a96c1e41
Open AI Gym http://gym.openai.com/
Richard Bellman, Dynamic Programming, (Dover Publications, Inc., New York, 2003).
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis, Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, AxXiv e-prints, Dec 2017.
G. A. Rummery, Mahesh Niranjan On-Line Q-Learning using Connectionist Systems, volume 37. University of Cambridge, Department of Engineering.
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Joshi, A.V. (2020). Dynamic Programming and Reinforcement Learning. In: Machine Learning and Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-26622-6_9
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DOI: https://doi.org/10.1007/978-3-030-26622-6_9
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