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91adefdb4b
* multicategorical dist and test * fixed List annotation * bernoulli dist and test * added distributions to preprocessing (needs testing) * fixed and tested distributions * added changelog and fixed ppo policy * minor fix * dist fixes, added test_spaces * clean up * modified changelog * additional fixes * minor changelog mod * hot encoding fix, flake8 clean up * lint tests * preprocessing fix * fixed bernoulli bug * removed commented prints * Update changelog.rst * included suggested modifications * linting fix * increased space dim * Update doc and tests Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
115 lines
2.8 KiB
ReStructuredText
115 lines
2.8 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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.. warning::
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The SAC model does not support ``stable_baselines3.ppo.policies`` because it uses double q-values
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and value estimation, as a result it must use its own policy models (see :ref:`sac_policies`).
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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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