UC_BERKELEY · COMPSCI · COURSE SHEET
COMPSCI 1853 creditsSee offeringsHas prerequisites
Deep Reinforcement Learning, Decision Making, and Control
CT Score
1500
#0 of 0 in COMPSCI
Difficulty
8/10
Workload
—h/wk
Median grade
A−
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 surveys0.6%
17.8%
20.1%
16.1%
9.8%
2.9%
3.5%
1.2%
0.6%
0.6%
0.6%
1.2%
4%
21.3%
A+
A
A-
B+
B
C+
C
C-
D+
D
D-
F
NP
P
By professor: same course, different game
PROFESSORSCOREGRADEHRS/WKTERMS
SL
Sergey Levine
1 community ratings
8
——Sections
No section data for this term yet.
In the tree
CS189/289A or equivalent is a prerequisite for the course. 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 185→unlocks 0 courses
No mapped courses require this one yet. See on the map →
Study groups
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