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CISC 474 Reinforcement Learning

CISC 474  Reinforcement Learning  Units: 3.00  

Formal and heuristic approaches to problem-solving, planning, knowledge representation and reasoning, Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo learning, function approximation, integration of learning and planning. Implementing simple examples of logical reasoning, clustering or classification.

Learning Hours: 120 (36 Lecture, 12 Group Learning, 72 Private Study)  
Requirements: Prerequisite Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a 'Pass' (obtained in Winter 2020) in CISC 352.  
Offering Faculty: Faculty of Arts and Science