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ray/rllib/agents/sac
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Richard Liaw a2d2275ee1
Revert "[RLlib + Tune] Add placement group support to RLlib. (#14289)" (#14360) 
This reverts commit 6cd0cd3bd9.
2021-02-25 14:27:35 -08:00
..
tests Revert "[RLlib + Tune] Add placement group support to RLlib. (#14289)" (#14360) 2021-02-25 14:27:35 -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] Allow SAC to use custom models as Q- or policy nets and deprecate "state-preprocessor" for image spaces. (#13522) 2021-02-02 13:05:58 +01:00
sac_tf_model.py [RLlib] Extend on_learn_on_batch callback to allow for custom metrics to be added. (#13584) 2021-02-08 15:02:19 +01:00
sac_tf_policy.py [RLlib] Issue #13342: Add validate_spaces to MB-MPO. (#14038) 2021-02-11 11:36:53 +01:00
sac_torch_model.py [RLlib] Extend on_learn_on_batch callback to allow for custom metrics to be added. (#13584) 2021-02-08 15:02:19 +01:00
sac_torch_policy.py [RLlib] DDPG: Support simplex action space. (#14011) 2021-02-10 15:10:01 +01:00

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