Description
Instructor(s)/Supervisor(s)/Coordinator(s): Long CHENReinforcement learning (RL) is a computational learning approach where an agent tries to maximize the total amount of reward it receives while interacting with a complex and uncertain environment. It not only shows strong performance in lots of games (such as Go), but also becomes an essential technique in many today’s real-world applications (such as LLM training, and embodied AI). This course aims to teach the fundamentals and the advanced topics of RL. The course content includes the introduction of basic RL elements (including MDP, dynamic programming, policy iteration), value-based approaches (DQN), policy-based approaches (policy gradient, actor critic), and model-based RL. To better enhance the understanding, this course will also contain some Python/Pytorch implementations.