mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-16 21:10:08 +00:00
44f8218df0
* Split torch module code into torch_layers file * Updated reference to CNN * Change 'CxWxH' to 'CxHxW', as per common notion * Fix missing import in policies.py * Move PPOPolicy to OnlineActorCriticPolicy * Create OnPolicyRLModel from PPO, and make A2C and PPO inherit * Update A2C optimizer comment * Clean weight init scales for clarity * Fix A2C log_interval default parameter * Rename 'progress' to 'progress_remaining * Rename 'Models' to 'Algorithms' * Rename 'OnlineActorCriticPolicy' to 'ActorCriticPolicy' * Move static functions out from BaseAlgorithm * Move on/off_policy base algorithms to their own files * Add files for A2C/PPO * Fix docs * Fix pytype * Update documentation on OnPolicyAlgorithm * Add proper doctstring for on_policy rollout gathering * Add bit clarification on the mlppolicy/cnnpolicy naming * Move static function is_vectorized_policies to utils.py * Checking docstrings, pep8 fixes * Update changelog * Clean changelog * Remove policy warnings for sac/td3 * Add monitor_wrapper for OnPolicyAlgorithm. Clean tb logging variables. Add parameter keywords to OffPolicyAlgorithm super init Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
109 lines
2.6 KiB
ReStructuredText
109 lines
2.6 KiB
ReStructuredText
.. _sac:
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.. automodule:: stable_baselines3.sac
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SAC
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===
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`Soft Actor Critic (SAC) <https://spinningup.openai.com/en/latest/algorithms/sac.html>`_ Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.
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SAC is the successor of `Soft Q-Learning SQL <https://arxiv.org/abs/1702.08165>`_ and incorporates the double Q-learning trick from TD3.
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A key feature of SAC, and a major difference with common RL algorithms, is that it is trained to maximize a trade-off between expected return and entropy, a measure of randomness in the policy.
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.. rubric:: Available Policies
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.. autosummary::
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:nosignatures:
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MlpPolicy
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CnnPolicy
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Notes
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-----
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- Original paper: https://arxiv.org/abs/1801.01290
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- OpenAI Spinning Guide for SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
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- Original Implementation: https://github.com/haarnoja/sac
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- Blog post on using SAC with real robots: https://bair.berkeley.edu/blog/2018/12/14/sac/
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.. note::
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In our implementation, we use an entropy coefficient (as in OpenAI Spinning or Facebook Horizon),
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which is the equivalent to the inverse of reward scale in the original SAC paper.
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The main reason is that it avoids having too high errors when updating the Q functions.
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.. note::
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The default policies for SAC differ a bit from others MlpPolicy: it uses ReLU instead of tanh activation,
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to match the original paper
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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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.. code-block:: python
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import gym
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import numpy as np
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from stable_baselines3 import SAC
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from stable_baselines3.sac import MlpPolicy
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env = gym.make('Pendulum-v0')
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model = SAC(MlpPolicy, env, verbose=1)
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model.learn(total_timesteps=10000, log_interval=4)
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model.save("sac_pendulum")
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del model # remove to demonstrate saving and loading
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model = SAC.load("sac_pendulum")
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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, reward, done, info = env.step(action)
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env.render()
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if done:
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obs = env.reset()
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Parameters
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----------
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.. autoclass:: SAC
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:members:
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:inherited-members:
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.. _sac_policies:
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SAC Policies
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-------------
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.. autoclass:: MlpPolicy
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:members:
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:inherited-members:
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.. .. autoclass:: CnnPolicy
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.. :members:
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.. :inherited-members:
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