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This lecture covers the theory of Reinforcement Learning, the Exploration/Exploitation dilemma, Temporal Difference Learning, Eligibility Traces, and strategies for Continuous State/Action Spaces. It also introduces the Q-Learning algorithm, optimal paths, and the Bellman equation for multi-step horizons.