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Fall 2019 Semester
Sep 19, 2019
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CS 4180 - Reinforcement Learning
Introduces reinforcement learning and the Markov decision process (MDP) framework. Covers methods for planning and learning in MDPs such as dynamic programming, model-based methods, and model-free methods. Examines commonly used representations including deep-learning representations. Students are expected to have a working knowledge of probability, 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: Undergraduate
Schedule Types: Lecture

Computer Science Department

Course Attributes:
Computer&Info Sci

Restrictions:
Must be enrolled in one of the following Levels:     
      Undergraduate

Prerequisites:
Undergraduate level CS 3000 Minimum Grade of D- and (Undergraduate level ECON 2350 Minimum Grade of D- or Undergraduate level ENVR 2500 Minimum Grade of D- or Undergraduate level MATH 3081 Minimum Grade of D- or Undergraduate level PSYC 2320 Minimum Grade of D- or Undergraduate level CS 2810 Minimum Grade of D-) and (Undergraduate level MATH 2331 Minimum Grade of D- or Undergraduate level CS 2810 Minimum Grade of D-)

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