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CS885 Fall 2021 - Reinforcement Learning
There are many good references for machine learning. We will cover material in different textbooks. The first few textbooks are freely available online. Complementary readings in the textbooks are assigned for every lecture in the course schedule .
[SutBar] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction (2nd edition, 2018) freely available online [Sze] Csaba Szepesvari, Algorithms for Reinforcement Learning freely available online [ZB] Alex Zai and Brandon Brown, Deep Reinforcement Learning in Action (2nd edition, 2020) freely available online [GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (2016) freely available online [L] Maxim Lapan, Deep Reinforcement Learning Hands On (2020) [GK] Laura Graesser and Wah Loon Keng, Foundations of Deep Reinforcement Learning: Theory and Practice in Python (2020) [SigBuf] Olivier Sigaud and Olivier Buffet (editors), Markov Decision Processes in Artificial Intelligence (2013)[Put] Martin L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming (2008)[Ber] Dimitri P. Bertsekas, Dynamic Programming and Optimal Control (2017)[Pow] Warren B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality (2015)[RusNor] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (4th Edition) (2020)