CS 4180 — Reinforcement Learning

4 semester hoursUndergraduateLectureusually offered: fall, springtypical days: M/ThBostonTraditional

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.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 2023155100%0.0
Spring 2024191560%6.0
Fall 20241131587%2.0
Spring 20251151694%1.0
Spring 202611615107%0.0

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 66% · T 34% · W 16% · Th 50% · F 34%

Common patterns: MR (50% of sections), TF (34% of sections), MW (16% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Percentages are each professor's average share of the season's enrolled students in recent terms.

Links

Official catalog (CS course descriptions) · Student reviews on RateMyHusky · All CS courses · Plan it at numap.app