Reinforcement Learning

This course covers fundamental principles, algorithms, and implementations of reinforcement learning, including the design of computational agents based on machine learning and control theory. The typical methods include reinforcement algorithms, dynamic programming, approximate functions, and temporal difference learning for policy evaluation and control problems.