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, CMSC389Y, CMSC389Z
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