CS 542

Fall 2026 All Classes

All Classes

Statistical Reinforcement Learning

Credit: 4 hours.

Theory of reinforcement learning, with a focus on sample complexity analyses. Specific topics include MDP basics, finite-sample analyses of online (i.e., exploration) and offline (i.e., batch) RL with a tabular representation, finite-sample analyses of online and offline RL with function approximation, state abstraction theory, off-policy evaluation (importance sampling), and policy gradient. The course goal is to provide a comprehensive understanding of the statistical properties of RL under various settings (e.g., online vs offline), preparing the students for doing research in the area.

Prerequisite: Calculus, linear algebra, probability and statistics, and basic concepts of machine learning. Familiarity with (at least one of) the following topics is highly recommended: stochastic processes, numerical analysis, and theoretical computer science.

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CS 542 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			74766	Lecture-Discussion	S	2:00PM -3:15PM	WF	0216 Siebel Center for Comp Sci	Jiang, N	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 4 hours Section Info: For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister http://nanjiang.cs.illinois.edu/cs542/ Restriction(s): Restricted to Computer Science or Bioinformatics major(s). Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.