COMP 579. Reinforcement Learning.
Credits: 4
Offered by: Computer Science (Faculty of Science)
Terms offered: Winter 2027
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Description
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.
- 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.