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CS234

Reinforcement Learning

Computer Science ENGR - School of Engineering

Course Description

To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will briefly cover background on Markov decision processes and reinforcement learning, before focusing on some of the central problems, including scaling up to large domains and the exploration challenge. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning. Prerequisites: proficiency in python, CS 229 or equivalents or permission of the instructor; linear algebra, basic probability.

Grading Basis

ROP - Letter or Credit/No Credit

Min

3

Max

3

Course Repeatable for Degree Credit?

No

Course Component

Discussion

Enrollment Optional?

Yes

Course Component

Lecture

Enrollment Optional?

No

Does this course satisfy the University Language Requirement?

No

Programs

CS234 is a completion requirement for:
  • (from the following course set: )