COMP 4010
Introduction to Reinforcement Learning
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.
2025
Topics
- Math review, multi-armed bandit
- Dynamic programming methods
- Temporal difference learning
- Planning (Thanksgiving Monday)
- Environment demo
- Function approximation
- Actor-critic
- Deep reinforcement learning
Prerequisites & course information
Prerequisite(s): COMP 2402 , ( COMP 2404 or SYSC 3010 or SYSC 3110 ), ( MATH 1004 or MATH 1007), ( MATH 1104 or MATH 1107 ), and ( DATA 1517 or STAT 2507 ). Lectures three hours a week.