COMP 579 Reinforcement Learning (4 credits)

Overview

Computer Science (Sci) : 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.

Terms: This course is not scheduled for the 2020-2021 academic year.

Instructors: There are no professors associated with this course for the 2020-2021 academic year.

• 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.