CMSC389F
Special Topics in Computer Science; Reinforcement Learning
Prerequisites: CMSC216 and CMSC250 with a grade of C- or better. Provides a theory-centric introduction to Reinforcement Learning. Students will learn the key concepts and algorithms driving Reinforcement Learning, including Markov Decision Processes, Monte Carlo Learning, and Policy Gradient methods.
Sister Courses: CMSC389A, CMSC389B, CMSC389C, CMSC389E, CMSC389G, CMSC389I, CMSC389J, CMSC389K, CMSC389L, CMSC389M, CMSC389N, CMSC389O, CMSC389P, CMSC389Q, CMSC389R, CMSC389T, CMSC389U, CMSC389V, CMSC389W, CMSC389X, CMSC389Y, CMSC389Z
Past Semesters
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During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.
Average GPA of 3.62 between 85 students*
* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.