Johns Hopkins University 3 credits Computer Science
Tremendous success of reinforcement learning (RL) in a variety of settings from AlphaGo to LLMs makes it a critical area to study. This course will study classical aspects of RL as well as its modern counterparts. Topics will include Markov Decision Processes, dynamic programming, model-based and model-free RL, temporal difference learning, Monte Carlo methods, multi-armed bandits, policy optimization and other methods. Prerequisite(s): Students who have taken, or are currently enrolled in EN.601.679, are not eligible to take EN.601.479.;(((EN.601.464 OR EN.601.664) OR (EN.601.475 OR EN.601.675) OR (EN.601.482 OR EN.601.682)) AND (AS.110.201 OR AS.110.212 OR EN.553.291 OR EN.553.295) AND (EN.553.211 OR EN.553.310 OR EN.553.311 OR EN.553.420 OR EN.553.421))
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