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ray/rllib/agents/sac
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Julius Frost 0b1b6222bc
[rllib] Add merge_trainer_config arguments to trainer template (#17160)
2021-07-21 15:43:06 -07:00
..
tests [RLlib] Refactor: All tf static graph code should reside inside Policy class. (#17169) 2021-07-20 14:58:13 -04:00
__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] Add merge_trainer_config arguments to trainer template (#17160) 2021-07-21 15:43:06 -07:00
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] CQL BC loss fixes; PPO/PG/A2|3C action normalization fixes (#16531) 2021-06-30 12:32:11 +02:00
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] CQL BC loss fixes; PPO/PG/A2|3C action normalization fixes (#16531) 2021-06-30 12:32:11 +02:00

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