Table of Contents

Chapter 1 Introduction of Reinforcement Learning.- Chapter 2 Principles of RL Problems.- Chapter 3 Model-free Indirect RL: Monte Carlo.- Chapter 4 Model-Free Indirect RL: Temporal-Difference.- Chapter 5 Model-based Indirect RL: Dynamic Programming.- Chapter 6 Indirect RL with Function Approximation.- Chapter 7 Direct RL with Policy Gradient.- Chapter 8 Infinite Horizon Approximate Dynamic Programming.- Chapter 9 Finite Horizon ADP and State Constraints.- Chapter 10 Deep Reinforcement Learning.- Chapter 11 Advanced RL Topics.

About the Author

Prof. Shengbo Eben Li received his M.S. and Ph.D. degrees from Tsinghua University in 2006 and 2009. He is currently a professor at Tsinghua University in the interdisciplinary field of autonomous driving and artificial intelligence. Before joining Tsinghua University, he has worked at Stanford University, University of Michigan, and UC Berkeley. His active research interests include intelligent vehicles and driver assistance, deep reinforcement learning, optimal control and estimation, etc.

He has published more than 130 peer-reviewed papers in top-tier international journals and conferences. He is the recipient of best paper awards (finalists) of IEEE ITSC, ICCAS, IEEE ICUS, IEEE IV, L4DC, etc. He has received a number of important academic honors, including National Award for Technological Invention of China (2013), National Award for Progress in Sci & Tech of China (2018), Distinguished Young Scholar of Beijing NSF (2018), and Natural Science Award of Chinese Association of Automation (2021). He also serves as Board of Governor of IEEE ITS Society, Senior AE of IEEE OJ ITS, and AEs of IEEE ITSM, IEEE Trans ITS, Automotive Innovation, etc.