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* Update ppo.rst minor correction from A2C to PPO * Update changelog.rst * Update changelog.rst Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
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82 lines
2 KiB
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.. _ppo2:
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.. automodule:: stable_baselines3.ppo
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PPO
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===
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The `Proximal Policy Optimization <https://arxiv.org/abs/1707.06347>`_ algorithm combines ideas from A2C (having multiple workers)
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and TRPO (it uses a trust region to improve the actor).
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The main idea is that after an update, the new policy should be not too far form the old policy.
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For that, ppo uses clipping to avoid too large update.
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.. note::
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PPO contains several modifications from the original algorithm not documented
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by OpenAI: advantages are normalized and value function can be also clipped .
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Notes
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-----
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- Original paper: https://arxiv.org/abs/1707.06347
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- Clear explanation of PPO on Arxiv Insights channel: https://www.youtube.com/watch?v=5P7I-xPq8u8
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- OpenAI blog post: https://blog.openai.com/openai-baselines-ppo/
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- Spinning Up guide: https://spinningup.openai.com/en/latest/algorithms/ppo.html
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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 PPO agent on ``Pendulum-v0`` using 4 environments.
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.. code-block:: python
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import gym
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from stable_baselines3 import PPO
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from stable_baselines3.ppo import MlpPolicy
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from stable_baselines3.common.cmd_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 = PPO(MlpPolicy, env, verbose=1)
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model.learn(total_timesteps=25000)
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model.save("ppo_cartpole")
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del model # remove to demonstrate saving and loading
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model = PPO.load("ppo_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:: PPO
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:members:
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:inherited-members:
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