AE 598

Spring 2023 All Classes

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Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in aerospace engineering intended to augment existing formal courses. Topics and prerequisites vary for each section. See Class Schedule or departmental course information for both.

May be repeated in the same or separate terms if topics vary to a maximum of 12 hours.

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AE 598 class schedule data for spring 2023
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	70885	Online	ORL	ARRANGED	n.a.	n.a.	Tran, H	Part of Term: 1 Date Range: 01/17/23-05/03/23 Credit: 4 hours Section Title: Reinforcement Learning Section Info: Theory and practice of reinforcement learning (RL) algorithms with applications to control, robotics, and multi-agent systems. The goal is for students to understand: (1) RL algorithms and their implementation, (2) key theoretical concepts, and (3) when and how RL can be used for research applications. Topics include MDPs, value-based methods, policy methods, function approximation, and multi-agent reinforcement learning. Pre-reqs: CS 446 or equivalent; STAT 400 or equivalent; proficiency with Python. Restriction(s): Restricted to MS: Civil Engr - Online - UIUC, MCS:Computer Sci Online -UIUC, MS:Industrial Engr Online-UIUC, MS:Mechanical Engineerng -UIUC, MS:Env Engr CivilEngr ONL-UIUC, MS: Aerospace Engr-Online-UIUC, NDEG:Grad Nondegree-CE-UIUC, NDEG:Undergrad Nondeg-CE-UIUC, MCS: Computer Sci Online-UIUC, or MENG:Mech Engineering Onl-UIUC.
	49350	Lecture-Discussion	RL	10:00AM -11:50AM	TR	410B1 Engineering Hall	Tran, H	Part of Term: 1 Date Range: 01/17/23-05/03/23 Section Title: Reinforcement Learning Section Info: Theory and practice of reinforcement learning (RL) algorithms with applications to control, robotics, and multi-agent systems. The goal is for students to understand: (1) RL algorithms and their implementation, (2) key theoretical concepts, and (3) when and how RL can be used for research applications. Topics include MDPs, value-based methods, policy methods, function approximation, and multi-agent reinforcement learning. Pre-reqs: CS 446 or equivalent; STAT 400 or equivalent; proficiency with Python.