Metadata-Version: 2.1
Name: garage
Version: 2020.10.0rc5
Summary: A toolkit for reproducible reinforcement learning research
Home-page: https://github.com/rlworkgroup/garage
Author: Reinforcement Learning Working Group
License: MIT
Description: [![Docs](https://readthedocs.org/projects/garage/badge)](http://garage.readthedocs.org/en/latest/)
        [![Build Status](https://travis-ci.com/rlworkgroup/garage.svg?branch=master)](https://travis-ci.com/rlworkgroup/garage)
        ![Garage CI](https://github.com/rlworkgroup/garage/workflows/Garage%20CI/badge.svg?event=schedule)
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        [![PyPI version](https://badge.fury.io/py/garage.svg)](https://badge.fury.io/py/garage)
        
        # garage
        
        garage is a toolkit for developing and evaluating reinforcement learning
        algorithms, and an accompanying library of state-of-the-art implementations
        built using that toolkit.
        
        The toolkit provides wide range of modular tools for implementing RL algorithms,
        including:
        
        * Composable neural network models
        * Replay buffers
        * High-performance samplers
        * An expressive experiment definition interface
        * Tools for reproducibility (e.g. set a global random seed which all components
          respect)
        * Logging to many outputs, including TensorBoard
        * Reliable experiment checkpointing and resuming
        * Environment interfaces for many popular benchmark suites
        * Supporting for running garage in diverse environments, including always
          up-to-date Docker containers
        
        See the [latest documentation](https://garage.readthedocs.org/en/latest/) for
        getting started instructions and detailed APIs.
        
        ## Installation
        
        ```
        pip install --user garage
        ```
        
        ## Join the Community
        
        **Join the [garage-announce mailing list](https://groups.google.com/forum/#!forum/garage-announce/join)**
        for infrequent updates (<1/mo.) on the status of the project and new releases.
        
        Need some help? Want to ask garage is right for your project? Have a question
        which is not quite a bug and not quite a feature request?
        
        **Join the community Slack** by filling out
        [this Google Form](https://docs.google.com/forms/d/e/1FAIpQLSf4AXRIbA1cLGjku4lIRQ6btStWPeIMeG3J17i4_FhFQU8X0g/viewform).
        
        ## Algorithms
        
        The table below summarizes the algorithms available in garage.
        
        | Algorithm              | Framework(s)        |
        | ---------------------- | ------------------- |
        | CEM                    | numpy               |
        | CMA-ES                 | numpy               |
        | REINFORCE (a.k.a. VPG) | PyTorch, TensorFlow |
        | DDPG                   | PyTorch, TensorFlow |
        | DQN                    | TensorFlow          |
        | DDQN                   | TensorFlow          |
        | ERWR                   | TensorFlow          |
        | NPO                    | TensorFlow          |
        | PPO                    | PyTorch, TensorFlow |
        | REPS                   | TensorFlow          |
        | TD3                    | TensorFlow          |
        | TNPG                   | TensorFlow          |
        | TRPO                   | PyTorch, TensorFlow |
        | MAML                   | PyTorch             |
        | RL2                    | TensorFlow          |
        | PEARL                  | PyTorch             |
        | SAC                    | PyTorch             |
        | MTSAC                  | PyTorch             |
        | MTPPO                  | PyTorch, TensorFlow |
        | MTTRPO                 | PyTorch, TensorFlow |
        | Task Embedding         | TensorFlow          |
        | Behavioral Cloning     | PyTorch             |
        
        ## Supported Tools and Frameworks
        
        garage requires Python 3.6+. If you need Python 3.5 support, the last garage
        release to support Python 3.5 was
        [v2020.06](https://github.com/rlworkgroup/garage/releases/tag/v2020.06.0).
        
        The package is tested on Ubuntu 18.04. It is also known to run on Ubuntu 16.04,
        18.04, and 20.04, and recent versions of macOS using Homebrew. Windows users can
        install garage via WSL, or by making use of the Docker containers.
        
        We currently support [PyTorch](https://pytorch.org/) and
        [TensorFlow](https://www.tensorflow.org/) for implementing the neural network
        portions of RL algorithms, and additions of new framework support are always
        welcome. PyTorch modules can be found in the package
        [`garage.torch`](https://github.com/rlworkgroup/garage/tree/master/src/garage/torch)
        and TensorFlow modules can be found in the package
        [`garage.tf`](https://github.com/rlworkgroup/garage/tree/master/src/garage/tf).
        Algorithms which do not require neural networks are found in the package
        [`garage.np`](https://github.com/rlworkgroup/garage/tree/master/src/garage/np).
        
        The package is available for download on PyPI, and we ensure that it installs
        successfully into environments defined using
        [conda](https://docs.conda.io/en/latest/),
        [Pipenv](https://pipenv.readthedocs.io/en/latest/), and
        [virtualenv](https://virtualenv.pypa.io/en/latest/).
        
        ## Testing
        
        The most important feature of garage is its comprehensive automated unit test
        and benchmarking suite, which helps ensure that the algorithms and modules in
        garage maintain state-of-the-art performance as the software changes.
        
        Our testing strategy has three pillars:
        
        * **Automation:**
          We use continuous integration to test all modules and algorithms in garage
          before adding any change. The full installation and test suite is also run
          nightly, to detect regressions.
        * **Acceptance Testing:**
          Any commit which might change the performance of an algorithm is subjected to
          comprehensive benchmarks on the relevant algorithms before it is merged
        * **Benchmarks and Monitoring:**
          We benchmark the full suite of algorithms against their relevant benchmarks
          and widely-used implementations regularly, to detect regressions and
          improvements we may have missed.
        
        ## Supported Releases
        
        | Release | Build Status | Last date of support |
        | ------- | ------------ | -------------------- |
        | [v2020.06](https://github.com/rlworkgroup/garage/releases/tag/v2020.06.0) | [![Build Status](https://travis-ci.com/rlworkgroup/garage.svg?branch=release-2020.06)](https://travis-ci.com/rlworkgroup/garage) | February 28th, 2021 |
        | [v2019.10](https://github.com/rlworkgroup/garage/releases/tag/v2019.10.0) | [![Build Status](https://travis-ci.com/rlworkgroup/garage.svg?branch=release-2019.10)](https://travis-ci.com/rlworkgroup/garage) | October 31st, 2020 |
        
        Garage releases a new stable version approximately every 4 months, in February,
        June, and October. Maintenance releases have a stable API and dependency tree,
        and receive bug fixes and critical improvements but not new features. We
        currently support each release for a window of 8 months.
        
        ## Citing garage
        
        If you use garage for academic research, please cite the repository using the
        following BibTeX entry. You should update the `commit` field with the commit or
        release tag your publication uses.
        
        ```latex
        @misc{garage,
         author = {The garage contributors},
         title = {Garage: A toolkit for reproducible reinforcement learning research},
         year = {2019},
         publisher = {GitHub},
         journal = {GitHub repository},
         howpublished = {\url{https://github.com/rlworkgroup/garage}},
         commit = {be070842071f736eb24f28e4b902a9f144f5c97b}
        }
        ```
        
        ## Credits
        
        The original code for garage was adopted from predecessor project called
        [rllab](https://github.com/rll/rllab). The garage project is grateful for the
        contributions of the original rllab authors, and hopes to continue advancing the
        state of reproducibility in RL research in the same spirit.
        
        rllab was developed by Rocky Duan (UC Berkeley/Covariant), Peter Chen
        (UC Berkeley/Covariant), Rein Houthooft (UC Berkeley/Happy Elements), John
        Schulman (UC Berkeley/OpenAI), and Pieter Abbeel (UC Berkeley/Covariant).
        
        ---
        <p align="center" style="align-items:center; display:inline-block">Made with &#10084; &nbsp;at <a href="https://robotics.usc.edu/resl/" target="_blank"><img align="absmiddle" src="https://github.com/rlworkgroup/garage/blob/master/docs/_static/resl_logo.png?raw=true" height="60px"></a> and &nbsp;<a href="https://viterbischool.usc.edu/" target="_blank"><img align="absmiddle" src="https://github.com/rlworkgroup/garage/blob/master/docs/_static/viterbi_logo.png?raw=true" height="30px"></a></p>
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: gym
Provides-Extra: mujoco
Provides-Extra: dm_control
Provides-Extra: bullet
Provides-Extra: all
Provides-Extra: dev
