Deep Reinforcement Learning, Decision Making, and Control

CT Score

1500

#0 of 0 in COMPSCI

Difficulty

—/10

Workload

—h/wk

Median grade

from grade distribution

Would take again

—

n = 0 verified

ESTIMATEStarted from public course data. The first verified student reviews replace these numbers.

Grade distribution

Student-reported · course feedback surveys

15.4%

48.8%

11.5%

6.6%

6.5%

1.5%

0.7%

0.9%

0.7%

0.3%

0.2%

0.7%

0.9%

5.2%

A+

A-

B+

B-

C+

C-

D+

D-

By professor: same course, different game

PROFESSORSCOREGRADEHRS/WKTERMS

Professor ratings →

Sections

No section data for this term yet.

In the tree

COMPSCI189/289A or equivalent AND This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning, as well as a basic working knowledge of how to train deep neural networks (which is taught in CS182 and briefly covered in CS189).→COMPSCI 285→unlocks 0 courses

No mapped courses require this one yet. See on the map →

Study groups

0 active

🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.