mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-17 21:20:11 +00:00
b1322ff5d6
* fix cmd_util.py imports * Update changelog.rst Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
73 lines
1.5 KiB
ReStructuredText
73 lines
1.5 KiB
ReStructuredText
.. _a2c:
|
|
|
|
.. automodule:: stable_baselines3.a2c
|
|
|
|
|
|
A2C
|
|
====
|
|
|
|
A synchronous, deterministic variant of `Asynchronous Advantage Actor Critic (A3C) <https://arxiv.org/abs/1602.01783>`_.
|
|
It uses multiple workers to avoid the use of a replay buffer.
|
|
|
|
|
|
Notes
|
|
-----
|
|
|
|
- Original paper: https://arxiv.org/abs/1602.01783
|
|
- OpenAI blog post: https://openai.com/blog/baselines-acktr-a2c/
|
|
|
|
|
|
Can I use?
|
|
----------
|
|
|
|
- Recurrent policies: ✔️
|
|
- Multi processing: ✔️
|
|
- Gym spaces:
|
|
|
|
|
|
============= ====== ===========
|
|
Space Action Observation
|
|
============= ====== ===========
|
|
Discrete ✔️ ✔️
|
|
Box ✔️ ✔️
|
|
MultiDiscrete ✔️ ✔️
|
|
MultiBinary ✔️ ✔️
|
|
============= ====== ===========
|
|
|
|
|
|
Example
|
|
-------
|
|
|
|
Train a A2C agent on ``CartPole-v1`` using 4 environments.
|
|
|
|
.. code-block:: python
|
|
|
|
import gym
|
|
|
|
from stable_baselines3 import A2C
|
|
from stable_baselines3.a2c import MlpPolicy
|
|
from stable_baselines3.common.cmd_util import make_vec_env
|
|
|
|
# Parallel environments
|
|
env = make_vec_env('CartPole-v1', n_envs=4)
|
|
|
|
model = A2C(MlpPolicy, env, verbose=1)
|
|
model.learn(total_timesteps=25000)
|
|
model.save("a2c_cartpole")
|
|
|
|
del model # remove to demonstrate saving and loading
|
|
|
|
model = A2C.load("a2c_cartpole")
|
|
|
|
obs = env.reset()
|
|
while True:
|
|
action, _states = model.predict(obs)
|
|
obs, rewards, dones, info = env.step(action)
|
|
env.render()
|
|
|
|
Parameters
|
|
----------
|
|
|
|
.. autoclass:: A2C
|
|
:members:
|
|
:inherited-members:
|