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81 lines
1.9 KiB
ReStructuredText
81 lines
1.9 KiB
ReStructuredText
.. _a2c:
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.. automodule:: stable_baselines3.a2c
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A2C
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====
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A synchronous, deterministic variant of `Asynchronous Advantage Actor Critic (A3C) <https://arxiv.org/abs/1602.01783>`_.
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It uses multiple workers to avoid the use of a replay buffer.
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.. warning::
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If you find training unstable or want to match performance of stable-baselines A2C, consider using
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``RMSpropTFLike`` optimizer from ``stable_baselines3.common.sb2_compat.rmsprop_tf_like``.
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You can change optimizer with ``A2C(policy_kwargs=dict(optimizer_class=RMSpropTFLike))``.
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Read more `here <https://github.com/DLR-RM/stable-baselines3/pull/110#issuecomment-663255241>`_.
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Notes
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-----
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- Original paper: https://arxiv.org/abs/1602.01783
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- OpenAI blog post: https://openai.com/blog/baselines-acktr-a2c/
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Can I use?
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----------
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- Recurrent policies: ✔️
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- Multi processing: ✔️
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- Gym spaces:
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============= ====== ===========
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Space Action Observation
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============= ====== ===========
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Discrete ✔️ ✔️
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Box ✔️ ✔️
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MultiDiscrete ✔️ ✔️
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MultiBinary ✔️ ✔️
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============= ====== ===========
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Example
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-------
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Train a A2C agent on ``CartPole-v1`` using 4 environments.
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.. code-block:: python
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import gym
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from stable_baselines3 import A2C
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from stable_baselines3.a2c import MlpPolicy
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from stable_baselines3.common.env_util import make_vec_env
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# Parallel environments
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env = make_vec_env('CartPole-v1', n_envs=4)
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model = A2C(MlpPolicy, env, verbose=1)
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model.learn(total_timesteps=25000)
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model.save("a2c_cartpole")
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del model # remove to demonstrate saving and loading
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model = A2C.load("a2c_cartpole")
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obs = env.reset()
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while True:
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action, _states = model.predict(obs)
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obs, rewards, dones, info = env.step(action)
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env.render()
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Parameters
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----------
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.. autoclass:: A2C
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:members:
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:inherited-members:
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