Metadata-Version: 2.1
Name: stable-baselines3
Version: 0.9.0
Summary: Pytorch version of Stable Baselines, implementations of reinforcement learning algorithms.
Home-page: https://github.com/DLR-RM/stable-baselines3
Author: Antonin Raffin
Author-email: antonin.raffin@dlr.de
License: MIT
Description: 
        
        # Stable Baselines3
        
        Stable Baselines3 is a set of improved implementations of reinforcement learning algorithms in PyTorch. It is the next major version of [Stable Baselines](https://github.com/hill-a/stable-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.
        
        
        ## Links
        
        Repository:
        https://github.com/DLR-RM/stable-baselines3
        
        Medium article:
        https://medium.com/@araffin/df87c4b2fc82
        
        Documentation:
        https://stable-baselines3.readthedocs.io/en/master/
        
        RL Baselines3 Zoo:
        https://github.com/DLR-RM/rl-baselines3-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 PPO on a cartpole environment:
        
        ```python
        import gym
        
        from stable_baselines3 import PPO
        
        env = gym.make('CartPole-v1')
        
        model = PPO('MlpPolicy', env, verbose=1)
        model.learn(total_timesteps=10000)
        
        obs = env.reset()
        for i in range(1000):
            action, _states = model.predict(obs, deterministic=True)
            obs, reward, done, info = env.step(action)
            env.render()
            if done:
                obs = env.reset()
        ```
        
        Or just train a model with a one liner if [the environment is registered in Gym](https://github.com/openai/gym/wiki/Environments) and if [the policy is registered](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html):
        
        ```python
        from stable_baselines3 import PPO
        
        model = PPO('MlpPolicy', 'CartPole-v1').learn(10000)
        ```
        
        
Keywords: reinforcement-learning-algorithms reinforcement-learning machine-learning gym openai stable baselines toolbox python data-science
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
Provides-Extra: extra
