ray/rllib/agents/sac/__init__.py

from ray.rllib.agents.sac.sac import SACTrainer, DEFAULT_CONFIG
from ray.rllib.agents.sac.sac_tf_policy import SACTFPolicy
from ray.rllib.agents.sac.sac_torch_policy import SACTorchPolicy

from ray.rllib.agents.sac.rnnsac import RNNSACTrainer, \
                                        DEFAULT_CONFIG as RNNSAC_DEFAULT_CONFIG
from ray.rllib.agents.sac.rnnsac import RNNSACTorchPolicy

__all__ = [
    "DEFAULT_CONFIG", "SACTFPolicy", "SACTorchPolicy", "SACTrainer",
    "RNNSAC_DEFAULT_CONFIG", "RNNSACTorchPolicy", "RNNSACTrainer"
]
[rllib] Feature/soft actor critic v2 (#5328) * Add base for Soft Actor-Critic * Pick changes from old SAC branch * Update sac.py * First implementation of sac model * Remove unnecessary SAC imports * Prune unnecessary noise and exploration code * Implement SAC model and use that in SAC policy * runs but doesn't learn * clear state * fix batch size * Add missing alpha grads and vars * -200 by 2k timesteps * doc * lazy squash * one file * ignore tfp * revert done 2019-08-01 23:37:36 -07:00			`from ray.rllib.agents.sac.sac import SACTrainer, DEFAULT_CONFIG`
[RLlib] SAC Torch (incl. Atari learning) (#7984) * Policy-classes cleanup and torch/tf unification. - Make Policy abstract. - Add `action_dist` to call to `extra_action_out_fn` (necessary for PPO torch). - Move some methods and vars to base Policy (from TFPolicy): num_state_tensors, ACTION_PROB, ACTION_LOGP and some more. * Fix `clip_action` import from Policy (should probably be moved into utils altogether). * - Move `is_recurrent()` and `num_state_tensors()` into TFPolicy (from DynamicTFPolicy). - Add config to all Policy c'tor calls (as 3rd arg after obs and action spaces). * Add `config` to c'tor call to TFPolicy. * Add missing `config` to c'tor call to TFPolicy in marvil_policy.py. * Fix test_rollout_worker.py::MockPolicy and BadPolicy classes (Policy base class is now abstract). * Fix LINT errors in Policy classes. * Implement StatefulPolicy abstract methods in test cases: test_multi_agent_env.py. * policy.py LINT errors. * Create a simple TestPolicy to sub-class from when testing Policies (reduces code in some test cases). * policy.py - Remove abstractmethod from `apply_gradients` and `compute_gradients` (these are not required iff `learn_on_batch` implemented). - Fix docstring of `num_state_tensors`. * Make QMIX torch Policy a child of TorchPolicy (instead of Policy). * QMixPolicy add empty implementations of abstract Policy methods. * Store Policy's config in self.config in base Policy c'tor. * - Make only compute_actions in base Policy's an abstractmethod and provide pass implementation to all other methods if not defined. - Fix state_batches=None (most Policies don't have internal states). * Cartpole tf learning. * Cartpole tf AND torch learning (in ~ same ts). * Cartpole tf AND torch learning (in ~ same ts). 2 * Cartpole tf (torch syntax-broken) learning (in ~ same ts). 3 * Cartpole tf AND torch learning (in ~ same ts). 4 * Cartpole tf AND torch learning (in ~ same ts). 5 * Cartpole tf AND torch learning (in ~ same ts). 6 * Cartpole tf AND torch learning (in ~ same ts). Pendulum tf learning. * WIP. * WIP. * SAC torch learning Pendulum. * WIP. * SAC torch and tf learning Pendulum and Cartpole after cleanup. * WIP. * LINT. * LINT. * SAC: Move policy.target_model to policy.device as well. * Fixes and cleanup. * Fix data-format of tf keras Conv2d layers (broken for some tf-versions which have data_format="channels_first" as default). * Fixes and LINT. * Fixes and LINT. * Fix and LINT. * WIP. * Test fixes and LINT. * Fixes and LINT. Co-authored-by: Sven Mika <sven@Svens-MacBook-Pro.local> 2020-04-15 13:25:16 +02:00			`from ray.rllib.agents.sac.sac_tf_policy import SACTFPolicy`
			`from ray.rllib.agents.sac.sac_torch_policy import SACTorchPolicy`
[rllib] Feature/soft actor critic v2 (#5328) * Add base for Soft Actor-Critic * Pick changes from old SAC branch * Update sac.py * First implementation of sac model * Remove unnecessary SAC imports * Prune unnecessary noise and exploration code * Implement SAC model and use that in SAC policy * runs but doesn't learn * clear state * fix batch size * Add missing alpha grads and vars * -200 by 2k timesteps * doc * lazy squash * one file * ignore tfp * revert done 2019-08-01 23:37:36 -07:00
[RLlib] Add RNN-SAC agent (#16577) Shoutout to @ddworak94 :) 2021-07-25 16:04:52 +02:00			`from ray.rllib.agents.sac.rnnsac import RNNSACTrainer, \`
			`DEFAULT_CONFIG as RNNSAC_DEFAULT_CONFIG`
			`from ray.rllib.agents.sac.rnnsac import RNNSACTorchPolicy`

[rllib] Feature/soft actor critic v2 (#5328) * Add base for Soft Actor-Critic * Pick changes from old SAC branch * Update sac.py * First implementation of sac model * Remove unnecessary SAC imports * Prune unnecessary noise and exploration code * Implement SAC model and use that in SAC policy * runs but doesn't learn * clear state * fix batch size * Add missing alpha grads and vars * -200 by 2k timesteps * doc * lazy squash * one file * ignore tfp * revert done 2019-08-01 23:37:36 -07:00			`__all__ = [`
[RLlib] Add RNN-SAC agent (#16577) Shoutout to @ddworak94 :) 2021-07-25 16:04:52 +02:00			`"DEFAULT_CONFIG", "SACTFPolicy", "SACTorchPolicy", "SACTrainer",`
			`"RNNSAC_DEFAULT_CONFIG", "RNNSACTorchPolicy", "RNNSACTrainer"`
[rllib] Feature/soft actor critic v2 (#5328) * Add base for Soft Actor-Critic * Pick changes from old SAC branch * Update sac.py * First implementation of sac model * Remove unnecessary SAC imports * Prune unnecessary noise and exploration code * Implement SAC model and use that in SAC policy * runs but doesn't learn * clear state * fix batch size * Add missing alpha grads and vars * -200 by 2k timesteps * doc * lazy squash * one file * ignore tfp * revert done 2019-08-01 23:37:36 -07:00			`]`