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https://github.com/takuseno/minerva
An out-of-the-box GUI tool for offline deep reinforcement learning
https://github.com/takuseno/minerva
deep-learning deep-reinforcement-learning offline-rl pytorch
Last synced: 22 days ago
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An out-of-the-box GUI tool for offline deep reinforcement learning
- Host: GitHub
- URL: https://github.com/takuseno/minerva
- Owner: takuseno
- License: mit
- Created: 2020-08-07T07:35:57.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-05-29T11:12:34.000Z (over 3 years ago)
- Last Synced: 2024-10-02T21:17:38.089Z (about 1 month ago)
- Topics: deep-learning, deep-reinforcement-learning, offline-rl, pytorch
- Language: JavaScript
- Homepage: https://takuseno.github.io/minerva
- Size: 4.25 MB
- Stars: 94
- Watchers: 6
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-offline-rl - MINERVA: An out-of-the-box GUI tool for data-driven deep reinforcement learning - ui.readthedocs.io/en/v0.20/)] (Open Source Software/Implementations / Off-Policy Evaluation and Learning: Applications)
README
# MINERVA: An out-of-the-box GUI tool for offline deep reinforcement learning
[](https://badge.fury.io/py/minerva-ui)
[](https://hub.docker.com/r/takuseno/minerva)
[](https://minerva-ui.readthedocs.io/en/latest/?badge=latest)
[](https://codeclimate.com/github/takuseno/minerva/maintainability)
[](https://codecov.io/gh/takuseno/minerva)
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.
Documentation: https://minerva-ui.readthedocs.io
Chat: [](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.