| Name | minari JSON |
| Version |
0.4.3
JSON |
| download |
| home_page | |
| Summary | A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities. |
| upload_time | 2024-01-27 14:47:55 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | MIT License |
| keywords |
reinforcement learning
offline rl
rl
ai
gymnasium
farama
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://pre-commit.com/) [](https://github.com/psf/black)
<p align="center">
<img src="minari-text.png" width="500px"/>
</p>
Minari is a Python library for conducting research in offline reinforcement learning, akin to an offline version of Gymnasium or an offline RL version of HuggingFace's datasets library. This library is currently in beta.
The documentation website is at [minari.farama.org](https://minari.farama.org/main/). We also have a public discord server (which we use for Q&A and to coordinate development work) that you can join here: https://discord.gg/bnJ6kubTg6.
Note: Minari was previously developed under the name Kabuki.
## Installation
To install Minari from [PyPI](https://pypi.org/project/minari/):
```bash
pip install minari
```
Note that currently Minari is under a beta release. If you'd like to start testing or contribute to Minari please install this project from source with:
```
git clone https://github.com/Farama-Foundation/Minari.git
cd Minari
pip install -e .
```
## Command Line API
To check available remote datasets:
```bash
minari list remote
```
To download a dataset:
```bash
minari download door-human-v1
```
To check available local datasets:
```bash
minari list local
```
To show the details of a dataset:
```bash
minari show door-human-v1
```
For the list of commands:
```bash
minari --help
```
## Basic Usage
### Reading a dataset
```python
import minari
dataset = minari.load_dataset("door-human-v1")
for episode_data in dataset.iterate_episodes():
...
```
### Writing a dataset
```python
import minari
import gymnasium as gym
from minari import DataCollector
env = gym.make('LunarLander-v2')
env = DataCollector(env)
for _ in range(100):
env.reset()
done = False
while not done:
action = ...
obs, rew, terminated, truncated, info = env.step(action)
done = terminated or truncated
dataset = env.create_dataset("LunarLander-v2-test-v0")
```
For other examples, see [Basic Usage](https://minari.farama.org/main/content/basic_usage/). For a complete tutorial on how to create new datasets using Minari, see our [Pointmaze D4RL Dataset](https://minari.farama.org/main/tutorials/dataset_creation/point_maze_dataset/) tutorial, which re-creates the Maze2D datasets from [D4RL](https://github.com/Farama-Foundation/D4RL).
## Project Maintainers
Main Contributors: [Rodrigo Perez-Vicente](https://github.com/rodrigodelazcano), [Omar Younis](https://github.com/younik), [John Balis](https://github.com/balisujohn)
Maintenance for this project is also contributed by the broader Farama team: [farama.org/team](https://farama.org/team).
___
_Minari is a shortening of Minarai, the Japanese word for "learning by observation"._
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