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<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.
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.
## Installation
To install Minari from [PyPI](https://pypi.org/project/minari/):
```bash
pip install minari
```
This will install the minimum required dependencies. Additional dependencies will be prompted for installation based on your use case. To install all dependencies at once, use:
```bash
pip install "minari[all]"
```
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 ".[all]"
```
## Command Line API
To check available remote datasets:
```bash
minari list remote
```
To download a dataset:
```bash
minari download D4RL/door/human-v2
```
To check available local datasets:
```bash
minari list local
```
To show the details of a dataset:
```bash
minari show D4RL/door/human-v2
```
For the list of commands:
```bash
minari --help
```
## Basic Usage
### Reading a dataset
```python
import minari
dataset = minari.load_dataset("D4RL/door/human-v2")
for episode_data in dataset.iterate_episodes():
observations = episode_data.observations
actions = episode_data.actions
rewards = episode_data.rewards
terminations = episode_data.terminations
truncations = episode_data.truncations
infos = episode_data.infos
...
```
### Writing a dataset
```python
import minari
import gymnasium as gym
from minari import DataCollector
env = gym.make('FrozenLake-v1')
env = DataCollector(env)
for _ in range(100):
env.reset()
done = False
while not done:
action = env.action_space.sample() # <- use your policy here
obs, rew, terminated, truncated, info = env.step(action)
done = terminated or truncated
dataset = env.create_dataset("frozenlake/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).
## Citation
If you use Minari, please consider citing it:
```
@software{minari,
author = {Younis, Omar G. and Perez-Vicente, Rodrigo and Balis, John U. and Dudley, Will and Davey, Alex and Terry, Jordan K},
doi = {10.5281/zenodo.13767625},
month = sep,
publisher = {Zenodo},
title = {Minari},
url = {https://doi.org/10.5281/zenodo.13767625},
version = {0.5.0},
year = 2024,
bdsk-url-1 = {https://doi.org/10.5281/zenodo.13767625}
}
```
___
_Minari is a shortening of Minarai, the Japanese word for "learning by observation"._
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