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Fall 2019 Semester
Nov 14, 2019
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CS 5180 - Reinforcement Learning and Sequential Decision Making
Introduces reinforcement learning and the underlying computational frameworks and the Markov decision process framework. Covers a variety of reinforcement learning algorithms, including model-based, model-free, value function, policy gradient, actor-critic, and Monte Carlo methods. Examines commonly used representations including deep learning representations and approaches to partially observable problems. Students are expected to have a working knowledge of probability and linear algebra, to complete programming assignments, and to complete a course project that applies some form of reinforcement learning to a problem of interest.
4.000 Credit hours
4.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Computer Science Department

Course Attributes:
GSCS Computer & Info Science

Restrictions:
Must be enrolled in one of the following Levels:     
      Graduate
Must be enrolled in one of the following Colleges:     
      Khoury Coll of Comp Sciences

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