COMP 579 — Reinforcement Learning

Bandit algorithms, finite Markov decision processes, dynamic programming, Monte-Carlo Methods, temporal-difference learning, bootstrapping, planning, approximation methods, on versus off policy learning, policy gradient methods temporal abstraction and inverse reinforcement learning.

• Rating: 4.00 out of 5 from 2 student reviews
• Difficulty: 3.00 out of 5
• Credits: 4
• Faculty: Faculty of Science
• Department: Computer Science
• Prerequisite: A university level course in machine learning such as COMP 451 or COMP 551. Background in calculus, linear algebra, probability at the level of MATH 222, MATH 223, MATH 323, respectively.