Metadata-Version: 2.1
Name: minerva-ui
Version: 0.40
Summary: An out-of-the-box GUI tool for offline deep reinforcement learning
Home-page: https://github.com/takuseno/minerva
Author: Takuma Seno
Author-email: takuma.seno@gmail.com
License: MIT License
Description: <div align="center"><img src="assets/logo.jpg" width="800"/></div>
        
        # MINERVA: An out-of-the-box GUI tool for offline deep reinforcement learning
        [![PyPI version](https://badge.fury.io/py/minerva-ui.svg)](https://badge.fury.io/py/minerva-ui)
        ![test](https://github.com/takuseno/minerva/workflows/test/badge.svg)
        [![Docker Cloud Build Status](https://img.shields.io/docker/cloud/build/takuseno/minerva)](https://hub.docker.com/r/takuseno/minerva)
        [![Documentation Status](https://readthedocs.org/projects/minerva-ui/badge/?version=latest)](https://minerva-ui.readthedocs.io/en/latest/?badge=latest)
        [![Maintainability](https://api.codeclimate.com/v1/badges/0573d1557dcc6a4321f5/maintainability)](https://codeclimate.com/github/takuseno/minerva/maintainability)
        [![codecov](https://codecov.io/gh/takuseno/minerva/branch/master/graph/badge.svg?token=7OL530W7T4)](https://codecov.io/gh/takuseno/minerva)
        ![MIT](https://img.shields.io/badge/license-MIT-blue)
        
        MINERVA is an out-of-the-box GUI tool for offline deep reinforcement
        learning, designed for everyone including non-programmers to do reinforcement
        learning as a tool.
        
        <div align="center"><img src="assets/screenshot1.jpg" width="800"/></div>
        
        Documentation: https://minerva-ui.readthedocs.io
        
        Chat: [![Gitter](https://img.shields.io/gitter/room/d3rlpy/minerva)](https://gitter.im/d3rlpy/minerva)
        
        ## key features
        ### :zap: All You Need Is Dataset
        MINERVA only requires datasets to start offline deep reinforcement learning.
        Any combinations of vector observations and image observations with discrete
        actions and continuous actions are supported.
        
        ### :beginner: Stunning GUI
        MINERVA provides designed with intuitive GUI to let everyone lerverage extremely
        powerful algorithms without barriers. The GUI is developed as a Single Page
        Application (SPA) to make it work in the eye-opening speed.
        
        ### :rocket: Powerful Algorithm
        MINERVA is powered by [d3rlpy](https://github.com/takuseno/d3rlpy), a powerful
        offline deep reinforcement learning library for Python, to provide
        extremely powerful algorithms in an out-of-the-box way. The trained policy can
        be exported as [TorchScript](https://pytorch.org/docs/stable/jit.html) and
        [ONNX](https://onnx.ai/).
        
        ## installation
        ### PyPI
        ```
        $ pip install minerva-ui
        ```
        
        ### Docker
        ```
        $ docker run -d --gpus all -p 9000:9000 --name minerva takuseno/minerva:latest
        ```
        
        ## update guide
        
        If you update MINERVA, the database schema should be also updated as follows:
        ```
        $ pip install -U minerva-ui
        $ minerva upgrade-db
        ```
        
        ## usage
        ### run server
        At the first time, `~/.minerva` will be automatically created to store
        database, uploaded datasets and training metrics.
        ```
        $ minerva run
        ```
        By default, you can access to MINERVA interface at http://localhost:9000 .
        You can change the host and port with `--host` and `--port` arguments
        respectively.
        
        ### delete data
        You can delete entire data (`~/.minerva`) as follows:
        ```
        $ minerva clean
        ```
        
        ## contributions
        ### build
        ```
        $ npm install
        $ npm run build
        ```
        
        ### coding style
        This repository is fully formatted with [yapf](https://github.com/google/yapf)
        and [standard](https://github.com/standard/standard).
        You can format the entire scripts as follows:
        ```
        $ ./scripts/format
        ```
        
        ### lint
        This repository is fully analyzed with [Pylint](https://github.com/PyCQA/pylint),
        [ESLint](https://github.com/eslint/eslint) and [sass-lint](https://github.com/sasstools/sass-lint).
        You can run analysis as follows:
        ```
        $ ./scripts/lint
        ```
        
        ### test
        The unit tests are provided as much as possible.
        This repository is using `pytest-cov` instead of `pytest`.
        You can run the entire tests as follows:
        ```
        $ ./scripts/test
        ```
        
        ## acknowledgement
        This work is supported by Information-technology Promotion Agency, Japan
        (IPA), Exploratory IT Human Resources Project (MITOU Program) in the fiscal
        year 2020.
        
        The concept of the GUI software for deep reinforcement learning is inspired by
        [DeepAnalyzer](https://ghelia.com/en/product/) from Ghelia inc.
        I'm showing the great respect to the team here.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Requires-Python: >=3.5.0
Description-Content-Type: text/markdown
