To help you get started, we've collected the most common ways that stable-baselines is being used within popular public projects.

harvard-edge / quarl / stable-baselines / stable_baselines / common / distributions.py View on Github

def sample(self):
        return tf.stack([p.sample() for p in self.categoricals], axis=-1)

    @classmethod
    def fromflat(cls, flat):
        """
        Create an instance of this from new logits values

        :param flat: ([float]) the multi categorical logits input
        :return: (ProbabilityDistribution) the instance from the given multi categorical input
        """
        raise NotImplementedError


class DiagGaussianProbabilityDistribution(ProbabilityDistribution):
    def __init__(self, flat):
        """
        Probability distributions from multivariate Gaussian input

        :param flat: ([float]) the multivariate Gaussian input data
        """
        self.flat = flat
        mean, logstd = tf.split(axis=len(flat.shape) - 1, num_or_size_splits=2, value=flat)
        self.mean = mean
        self.logstd = logstd
        self.std = tf.exp(logstd)
        super(DiagGaussianProbabilityDistribution, self).__init__()

    def flatparam(self):
        return self.flat

View more ways to use stable-baselines

WARNING: This package is in maintenance mode, please use Stable-Baselines3 (SB3) for an up-to-date version. You can find a migration guide in SB3 documentation.

Stable Baselines

Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.

These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.

Main differences with OpenAI Baselines

This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups:

• Unified structure for all algorithms
• PEP8 compliant (unified code style)
• Documented functions and classes
• More tests & more code coverage
• Additional algorithms: SAC and TD3 (+ HER support for DQN, DDPG, SAC and TD3)

Links

Repository: https://github.com/hill-a/stable-baselines

Medium article: https://medium.com/@araffin/df87c4b2fc82

Documentation: https://stable-baselines.readthedocs.io/en/master/

RL Baselines Zoo: https://github.com/araffin/rl-baselines-zoo

Quick example

Most of the library tries to follow a sklearn-like syntax for the Reinforcement Learning algorithms using Gym.

Here is a quick example of how to train and run PPO2 on a cartpole environment:

import gym

from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO2

env = gym.make('CartPole-v1')
# Optional: PPO2 requires a vectorized environment to run
# the env is now wrapped automatically when passing it to the constructor
# env = DummyVecEnv([lambda: env])

model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=10000)

obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render()

Or just train a model with a one liner if the environment is registered in Gym and if the policy is registered:

from stable_baselines import PPO2

model = PPO2('MlpPolicy', 'CartPole-v1').learn(10000)