mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-16 21:10:08 +00:00
82 lines
1.9 KiB
ReStructuredText
82 lines
1.9 KiB
ReStructuredText
.. _ppo2:
|
|
|
|
.. automodule:: stable_baselines3.ppo
|
|
|
|
PPO
|
|
===
|
|
|
|
The `Proximal Policy Optimization <https://arxiv.org/abs/1707.06347>`_ algorithm combines ideas from A2C (having multiple workers)
|
|
and TRPO (it uses a trust region to improve the actor).
|
|
|
|
The main idea is that after an update, the new policy should be not too far form the old policy.
|
|
For that, ppo uses clipping to avoid too large update.
|
|
|
|
|
|
.. note::
|
|
|
|
PPO contains several modifications from the original algorithm not documented
|
|
by OpenAI: advantages are normalized and value function can be also clipped .
|
|
|
|
|
|
Notes
|
|
-----
|
|
|
|
- Original paper: https://arxiv.org/abs/1707.06347
|
|
- Clear explanation of PPO on Arxiv Insights channel: https://www.youtube.com/watch?v=5P7I-xPq8u8
|
|
- OpenAI blog post: https://blog.openai.com/openai-baselines-ppo/
|
|
- Spinning Up guide: https://spinningup.openai.com/en/latest/algorithms/ppo.html
|
|
|
|
|
|
Can I use?
|
|
----------
|
|
|
|
- Recurrent policies: ❌
|
|
- Multi processing: ✔️
|
|
- Gym spaces:
|
|
|
|
|
|
============= ====== ===========
|
|
Space Action Observation
|
|
============= ====== ===========
|
|
Discrete ❌ ❌
|
|
Box ✔️ ✔️
|
|
MultiDiscrete ❌ ❌
|
|
MultiBinary ❌ ❌
|
|
============= ====== ===========
|
|
|
|
Example
|
|
-------
|
|
|
|
Train a PPO agent on ``Pendulum-v0`` using 4 environments.
|
|
|
|
.. code-block:: python
|
|
|
|
import gym
|
|
|
|
from stable_baselines3 import A2C
|
|
from stable_baselines3.ppo import MlpPolicy
|
|
from stable_baselines3.common.cmd_utils import make_vec_env
|
|
|
|
# Parallel environments
|
|
env = make_vec_env('CartPole-v1', n_envs=4)
|
|
|
|
model = PPO(MlpPolicy, env, verbose=1)
|
|
model.learn(total_timesteps=25000)
|
|
model.save("ppo_cartpole")
|
|
|
|
del model # remove to demonstrate saving and loading
|
|
|
|
model = PPO.load("ppo_cartpole")
|
|
|
|
obs = env.reset()
|
|
while True:
|
|
action, _states = model.predict(obs)
|
|
obs, rewards, dones, info = env.step(action)
|
|
env.render()
|
|
|
|
Parameters
|
|
----------
|
|
|
|
.. autoclass:: PPO
|
|
:members:
|
|
:inherited-members:
|