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ray/rllib/agents/sac
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gjoliver d81885c1f1
[RLlib] Fix all the CI tests that were broken by is_training and replay buffer changes; re-comment-in the failing RLlib tests (#19809) 
* Fix DDPG, since it is based on GenericOffPolicyTrainer.

* Fix QMix, SAC, and MADDPA too.

* Undo QMix change.

* Fix DQN input batch type. Always use SampleBatch.

* apex ddpg should not use replay_buffer_config yet.

* Make eager tf policy to use SampleBatch.

* lint

* LINT.

* Re-enable RLlib broken tests to make sure things work ok now.

* fixes.

Co-authored-by: sven1977 <svenmika1977@gmail.com>
2021-10-28 18:06:47 +02:00
..
tests [RLlib] Issue 18418: SAC w/ dict space not working. (#19101) 2021-10-06 09:05:50 +02:00
__init__.py [RLlib] Add RNN-SAC agent (#16577) 2021-07-25 10:04:52 -04:00
README.md [RLlib] Improved Documentation for PPO, DDPG, and SAC (#12943) 2020-12-24 09:31:35 -05:00
rnnsac.py [RLlib] Allow n-step > 1 and prio. replay for R2D2 and RNNSAC. (#18939) 2021-09-29 21:31:34 +02:00
rnnsac_torch_model.py [RLlib] Add RNN-SAC agent (#16577) 2021-07-25 10:04:52 -04:00
rnnsac_torch_policy.py [RLlib] Issue 18812: Torch multi-GPU stats not protected against race conditions. (#18937) 2021-10-04 13:29:00 +02:00
sac.py [RLlib] Fix all the CI tests that were broken by is_training and replay buffer changes; re-comment-in the failing RLlib tests (#19809) 2021-10-28 18:06:47 +02:00
sac_tf_model.py [RLlib] Unify the way we create local replay buffer for all agents (#19627) 2021-10-26 20:56:02 +02:00
sac_tf_policy.py [RLlib] Issue 18418: SAC w/ dict space not working. (#19101) 2021-10-06 09:05:50 +02:00
sac_torch_model.py [RLlib] Unify the way we create local replay buffer for all agents (#19627) 2021-10-26 20:56:02 +02:00
sac_torch_policy.py [RLlib] Issue 18812: Torch multi-GPU stats not protected against race conditions. (#18937) 2021-10-04 13:29:00 +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