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COMP 579 Reinforcement Learning (4 unit茅s)

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Offered by: Informatique (Sciences)

Vue d'ensemble

Informatique (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: Hiver 2022

Instructors: Precup, Doina (Winter)

  • 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.

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