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ray/rllib/agents/sac
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Sven Mika 99ae7bae05
[RLlib] JAXPolicy prep. PR #1. (#13077)
2020-12-26 20:14:18 -05:00
..
tests [RLlib] Issue 11591: SAC loss does not use PR-weights in critic loss term. (#12394) 2020-11-25 11:28:46 -08:00
__init__.py [RLlib] SAC algo cleanup. (#10825) 2020-09-20 11:27:02 +02:00
README.md [RLlib] Improved Documentation for PPO, DDPG, and SAC (#12943) 2020-12-24 09:31:35 -05:00
sac.py [RLlib] Fix most remaining RLlib algos for running with trajectory view API. (#12366) 2020-12-01 17:41:10 -08:00
sac_tf_model.py [RLlib] Support Simplex action spaces for SAC (torch and tf). (#11909) 2020-11-11 18:45:28 +01:00
sac_tf_policy.py [RLlib] Issue 11591: SAC loss does not use PR-weights in critic loss term. (#12394) 2020-11-25 11:28:46 -08:00
sac_torch_model.py [RLlib] Support Simplex action spaces for SAC (torch and tf). (#11909) 2020-11-11 18:45:28 +01:00
sac_torch_policy.py [RLlib] JAXPolicy prep. PR #1. (#13077) 2020-12-26 20:14:18 -05:00

README.md

Soft Actor Critic (SAC)

Overview

SAC is a SOTA model-free off-policy RL algorithm that performs remarkably well on continuous-control domains. SAC employs an actor-critic framework and combats high sample complexity and training stability via learning based on a maximum-entropy framework. Unlike the standard RL objective which aims to maximize sum of reward into the future, SAC seeks to optimize sum of rewards as well as expected entropy over the current policy. In addition to optimizing over an actor and critic with entropy-based objectives, SAC also optimizes for the entropy coeffcient.

Documentation & Implementation:

Soft Actor-Critic Algorithm (SAC) with also discrete-action support.

Detailed Documentation

Implementation