.. _sacd:

.. automodule:: sb3_contrib.sacd


SACD
====


`Soft Actor Critic Discrete (SACD) <https://arxiv.org/abs/1910.07207>`_ is a modification of the original Soft Actor Critic Algorithm for discrete action spaces.

.. rubric:: Available Policies

.. autosummary::
    :nosignatures:

    MlpPolicy
    CnnPolicy
    MultiInputPolicy


Notes
-----

- Original paper: https://arxiv.org/abs/1910.07207
- Original Implementation: https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch


Can I use?
----------

-  Recurrent policies: ❌
-  Multi processing: ✔️
-  Gym spaces:


============= ====== ===========
Space         Action Observation
============= ====== ===========
Discrete      ✔️       ✔️
Box           ❌      ✔️
MultiDiscrete ❌      ✔️
MultiBinary   ❌      ✔️
Dict          ❌      ✔️
============= ====== ===========


Example
-------
.. code-block:: python

  import gymnasium as gym

  from sb3_contrib import SACD

  env = gym.make("CartPole-v1", render_mode="rgb_array")

  model = SACD("MlpPolicy", env, verbose=1, policy_kwargs=dict(net_arch=[64,64]))
  model.learn(total_timesteps=20_000)
  model.save("sacd_cartpole")

  del model # remove to demonstrate saving and loading

  model = SACD.load("sac_cartpole")

  obs, info = env.reset()
  while True:
      action, _states = model.predict(obs, deterministic=True)
      obs, reward, terminated, truncated, info = env.step(action)
      if terminated or truncated:
          obs, info = env.reset()



Parameters
----------

.. autoclass:: SACD
  :members:
  :inherited-members:

.. _sac_policies:

SACD Policies
-------------

.. autoclass:: MlpPolicy
  :members:
  :inherited-members:

.. autoclass:: stable_baselines3.sac.policies.SACPolicy
  :members:
  :noindex:

.. autoclass:: CnnPolicy
  :members:

.. autoclass:: MultiInputPolicy
  :members: