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This lecture from Memorial University's Computer Science 3200/6980 (Winter 2025) covers key reinforcement learning concepts, focusing on Temporal Difference Learning. Learn about on-policy vs off-policy methods, epsilon-soft implementations, SARSA, Q-Learning, and the differences between tabular and deep reinforcement learning through practical examples like the Cliff problem. The second half includes exam preparation guidance and a comprehensive demonstration of Assignment 5, complete with code walkthrough. Taught by Professor David Churchill, this session is part of the Introduction to Artificial Intelligence course that applies algorithmic techniques to game-based problem solving.
