This course focuses on topics related to reinforcement learning. The course will cover making decisions under uncertainty, Markov decision processes, dynamic programming, temporal-difference learning, eligibility traces, value function approximation methods, Monte Carlo reinforcement learning methods, and the integration of learning and planning.Weekly
This course focuses on topics related to reinforcement learning. The course will cover making decisions under uncertainty, Markov decision processes, dynamic programming, temporal-difference learning, eligibility traces, value function approximation methods, Monte Carlo reinforcement learning methods, and the integration of learning and planning.Weekly
