Codesota · Models · SACUC Berkeley1 results · 1 benchmarks
Model card

SAC.

UC Berkeleyopen-sourceSoft Actor-Critic

Off-policy max-entropy RL for continuous control

§ 02 · Benchmarks

Every benchmark SAC has a recorded score for.

#BenchmarkArea · TaskMetricValueRankDateSource
01MuJoCoRobotics, Control & RL · Continuous Controlaverage-return5179.00#2/122026-04-20source ↗
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 03 · Strengths by area

Where SAC actually performs.

Robotics, Control & RL
1
benchmark
avg rank #2.0
§ 05 · Related models

Other UC Berkeley models scored on Codesota.

SAC (state-based)
1 result
CQL (Conservative Q-Learning)
0 results
IQL (Implicit Q-Learning)
0 results
Octo-Base
0 results
§ 06 · Sources & freshness

Where these numbers come from.

codesota-api
1
result
1 of 1 rows marked verified.