Package: garage
garagegarage 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 modelsReplay buffersHigh-performance samplersAn expressive experiment definition interfaceTools for reproducibility (e.g. set a global random seed which all components
respect)Logging to many outputs, including TensorBoardReliable experiment checkpointing and resumingEnvironment interfaces for many popular benchmark suitesSupporting for running garage in diverse environments, including always
up-to-date Docker containersSee thelatest documentationfor
getting started instructions and detailed APIs.Installationpip install --user garageExamplesStarting from version v2020.10.0, garage comes packaged with examples. To get a
list of examples, run:garage examplesYou can also rungarage examples --help, or visitthe documentationfor even more details.Join the CommunityJoin thegarage-announce mailing listfor 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 Slackby filling outthis Google Form.AlgorithmsThe table below summarizes the algorithms available in garage.AlgorithmFramework(s)CEMnumpyCMA-ESnumpyREINFORCE (a.k.a. VPG)PyTorch, TensorFlowDDPGPyTorch, TensorFlowDQNPyTorch, TensorFlowDDQNPyTorch, TensorFlowERWRTensorFlowNPOTensorFlowPPOPyTorch, TensorFlowREPSTensorFlowTD3PyTorch, TensorFlowTNPGTensorFlowTRPOPyTorch, TensorFlowMAMLPyTorchRL2TensorFlowPEARLPyTorchSACPyTorchMTSACPyTorchMTPPOPyTorch, TensorFlowMTTRPOPyTorch, TensorFlowTask EmbeddingTensorFlowBehavioral CloningPyTorchSupported Tools and Frameworksgarage requires Python 3.6+. If you need Python 3.5 support, the last garage
release to support Python 3.5 wasv2020.06.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 supportPyTorchandTensorFlowfor implementing the neural network
portions of RL algorithms, and additions of new framework support are always
welcome. PyTorch modules can be found in the packagegarage.torchand TensorFlow modules can be found in the packagegarage.tf.
Algorithms which do not require neural networks are found in the packagegarage.np.The package is available for download on PyPI, and we ensure that it installs
successfully into environments defined usingconda,Pipenv, andvirtualenv.TestingThe 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 mergedBenchmarks 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 ReleasesReleaseBuild StatusLast date of supportv2021.03May 31st, 2021Maintenance 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 2 months.Citing garageIf you use garage for academic research, please cite the repository using the
following BibTeX entry. You should update thecommitfield with the commit or
release tag your publication uses.@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}}CreditsThe earliest code for garage was adopted from predecessor project calledrllab. 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. garage has
previously been supported by the Amazon Research Award "Watch, Practice, Learn,
Do: Unsupervised Learning of Robust and Composable Robot Motion Skills by Fusing
Expert Demonstrations with Robot Experience."Made with ❤  atand