Learn about designing and programming reinforcement learning agents to perform complex tasks in interactive environments. Topics include Markov decision processes, dynamic programming methods, Monte Carlo methods, temporal difference learning, prediction/control with function approximation, policy gradient, and deep reinforcement learning algorithms. Includes: Experiential Learning Activity Prerequisite(s): COMP 2402, (COMP 2404 or SYSC 3010 or SYSC 3110), MATH 1007 and (MATH 1104 or MATH 1107), STAT 2507. Lectures three hours a week. [0.5 credits]
Learn about designing and programming reinforcement learning agents to perform complex tasks in interactive environments. Topics include Markov decision processes, dynamic programming methods, Monte Carlo methods, temporal difference learning, prediction/control with function approximation, policy gradient, and deep reinforcement learning algorithms. Includes: Experiential Learning Activity Prerequisite(s): COMP 2402, (COMP 2404 or SYSC 3010 or SYSC 3110), MATH 1007 and (MATH 1104 or MATH 1107), STAT 2507. Lectures three hours a week. [0.5 credits]
