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Algorithmic and Theoretical Foundations of RL
Course Description
This course aims to provide an introduction to basic algorithmic and theoretical ingredients behind reinforcement learning,
including Markov decision process, value iteration and policy iteration, Monte Carlo and temporal-difference learning, value function
approximation, policy optimization, and online planning.
Main References
Reinforcement learning: an introduction by Richard S. Sutton and Andrew G. Barto, 2018
Mathematical foundation of reinforcement learning by Shiyu Zhao, 2022
Algorithms for decision making by Mikel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray, 2022
Introduction to Reinforcement Learning by David Silver, 2015
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