Resources
Books & Papers
“Reinforcement Learning: An introduction,” Second edition, in progress, by Richard S. Sutton and Andrew G. book
“Algorithms for Reinforcement Learning,” by Csaba Szepesvari book
“Reinforcement Learning and Dynamic Programming Using Function Approximators by Busoniu,” Babuska, De Schutter and Ernst book
“Perspectives of Approximate Dynamic Programming,” W. B. Powell, Annals of Operations Research, Annals of Operations Research (2012), Springer. tutorial
“Convergence of Q-learning: a simple proof,” by Melo paper
“Deterministic Policy Gradient Algorithms,” by Silver, Lever, Heess, Degris, Wierstra, Riedmiller, 2014 paper
appendices
Video Lectures
Courses
Berkeley's Deep Reinforcement Learning course CS 294 website including additional materials, presentations, video lectures
Course on Reinforcement Learning by Alessandro Lazaric from from the Electronic and Informatics Department of Politecnico di Milano website
Deep Reinforcement Learning and Control CMU 10703 website
Reinforcement Learning, Stanford, MS&E338, Benjamin Van Roy website
Optional paper for the project implementation
RL enviourment test and code
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