Id: 042128 Credits Min: 3 Credits Max: 3

Description

This course provides a solid introduction to the field of Reinforcement Learning (RL) and Decision Making. The students will learn about the basic blocks, main approached, and core challenges of Reinforcement Learning including tabular methods, Finite Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal-Difference learning, policy search, function approximation, exploration, and generalization. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning. In addition, students will advance their understanding and the field of RL through a final project.

Prerequisites

COMP.2010 Computing III, and MATH.3860 Probability and Statistics I, or Permission of Instructor.(Students seeking this permission are expected to have knowledge of calculus, linear algebra, and Python).

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