Reinforcement Learning

3 credits · Graduate School

Theory and practice of Reinforcement Learning (RL), Markov Decision Processes, Dynamic Programming, Monte Carlo methods, Temporal-Difference learning, Policy Gradient methods, and deep RL applications in games, robotics, and autonomous systems. [3]