PSYCH 119G
Reinforcement Learning in Biological and Artificial Agents
Lecture.
Winter 2027: 1 section. It is open. The oldest of these readings was 13 minutes ago.
Grades
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Workload and assignments
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What comes first
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Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
From the registrar's catalog
Lecture, three hours. Requisite: course 115. Recommended: course 119D. Introduction to fundamental principles of reinforcement learning theory, integrating biological and engineering perspectives. Review of brain systems that contribute to reinforcement learning and decision making. Introduction to common artificial intelligence algorithms for reinforcement learning. Comparative analysis of biological versus artificial reinforcement learning architectures. P/NP or letter grading.
Sections in Winter 2027
Seat counts as of . Seat readings are snapshots, not live availability. Their timestamps show when dibs last checked.
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Seats
Open45 seats left.
0 of 45 enrolled
Seat risk
low risk
From the people who took it
Student reviews
What taking PSYCH 119G was like: the work, the teaching, and what students wish they’d known.
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Seats over time in Winter 2027
How each lecture has filled since dibs first saw it.
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What a flat run means, and where a line starts
Flat between dots means it did not drift, it held and then jumped, and a line starts when dibs first saw that lecture rather than reaching back before it existed. A lecture that has never moved draws a flat line across the whole window, and one with two or three readings draws a line joining them, which is too few to read a trend from.
Lec 1 · Blair, H.T. · 0 of 45 enrolled
Unchanged over less than a minute
Both readings say 0 of 45.
How readings and alerts work
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