Metadata-Version: 2.1
Name: blobrl
Version: 0.1.3
Summary: Reinforcement learning with pytorch 
Home-page: https://github.com/french-ai/reinforcement
Author: french ai team
License: Apache-2.0
Download-URL: https://github.com/french-ai/reinforcement/archive/V0.1.3.tar.gz
Description: ï»¿BlobRL : Reinforcement Learning library with Pytorch
        ============
        
        
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        BlobRl is a modular library for reinforcement learning which works on pytorch.
        
        ## Installation
        
        ### Pytorch
        
        For installing *pytorch* follow [Quick Start Locally](https://pytorch.org/) for your config.
        
        ### BlobRL
        Install blobrl
        
        - to use it:
        
        ```bash
        pip install blobrl
        ```
        
        - to use it on notebook:
        
        ```bash
        pip install blobrl[notebook]
        ```
        
        - to help development:
        
        ```bash
        pip install blobrl[dev]
        ```
        
        ## Get Started
        ### Initialize environment
        ```python
        import gym
        env = gym.make("CartPole-v1")
        ```
        
        ### Initialize agent
        
        ```python
        from blobrl.agents import AgentRandom
        action_space = env.action_space
        observation_space = env.observation_space
        agent = AgentRandom(observation_space=observation_space, action_space=action_space)
        ```
        
        ### Train 
        
        Create Trainer
        ```python
        from blobrl import Trainer
        trainer = Trainer(environment=env, agent=agent, log_dir="./logs")
        ```
        Start training:
        ```python
        trainer.train(max_episode=100, nb_evaluation=4, render=True)
        ```
        
        ### Watch metrics
        Visualize training and evaluation metrics:
        ```bash
        tensorboard --logdir logs
        ```
        
        ## Environments
        
        We will use GYM environments for moments.
        
        Watch [TODO](./TODO.md#environments-list) for environments in coming.
        
        ## Agents
        
        Agent implemented:
        - AgentConstant, this agent choice one random action on start and will take her all next time
        - AgentRandom, this agent choice one random action each time
        - DQN, Deep Q Learning (Mnih *et al.*, [2013](https://arxiv.org/abs/1312.5602))
        - DoubleDQN, (van Hasselt *et al.*, [2016](https://arxiv.org/abs/1509.06461))
        - DuelingDQN, (Wang *et al.*, [2016](https://arxiv.org/abs/1511.06581))
        - CategoricalDQN, (Bellamare *et al.*, [2017](https://arxiv.org/abs/1707.06887))
        
        ## Examples
        
        You can find all examples [here](./examples)
        - [Jupyter exemple](./examples/example_jupyter.ipynb)
        - [Colab exemple](./examples/example_colab.ipynb)
        
        
        ## Results
        
        ![CartPoleExemple.gif](./results/ressources/cartpole.gif)
        
        You can see more [results](./results/README.md) and analysis
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: notebook
