Reinforcement Learning: TD Learning and SARSA Variants

This lecture covers the fundamentals of reinforcement learning, focusing on temporal difference (TD) learning and its various algorithms, particularly SARSA. The instructor begins with a review of TD learning, explaining its significance in solving the Bellman equation. The lecture introduces the concept of Q-values, which represent the expected return from state-action pairs, and discusses the importance of policies in reinforcement learning. The instructor elaborates on the SARSA algorithm, detailing its steps and the backup diagram used for updates. Variations of SARSA, including expected SARSA and Q-learning, are also examined, highlighting their differences in policy application. The lecture emphasizes the efficiency of TD methods over Monte Carlo methods in propagating information about rewards. The instructor concludes with a discussion on eligibility traces, which enhance learning speed, and engages students with exercises to reinforce their understanding of the concepts presented.