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A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities
| Date | Stars |
|---|---|
| 2026-07-24 | 1348 |
| 2026-07-25 | 1348 |
| 2026-07-28 | 1348 |
| 2026-07-30 | 1348 |
| 2026-07-31 | 1285 |
| 2026-08-02 | 1285 |
| 2026-08-05 | 1285 |
| 2026-08-06 | 1286 |
Today
+1 stars today
This week
-62 stars this week
This month
— stars this month
Momentum
14.0
growth rate 0.00%/day
[](https://badge.fury.io/py/minari)
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[](https://github.com/Farama-Foundation/Minari/releases)
<p align="center">
<a href = "https://minari.farama.org/" target= "_blank" > <img src="minari-text.png" width="500px"/> </a>
</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 --single-branch
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).
## Training Libraries Integrating Minari
- [TorchRL](https://github.com/pytorch/rl)
- [d3rlpy](https://github.com/takuseno/d3rlpy)
- [AgileRL](https://github.com/AgileRL/AgileRL)
## 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 Excerpt of 4,075 characters
Read on GitHub
Will Dudley · United Kingdom
176
Omar Younis · MILA
92
Rodrigo de Lazcano
46
Mark Towers · Anyscale · United Kingdom
15
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Jordan Terry
10
Elliot Tower · Morocco
6
Jet · @eluve-inc
5
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Manuel Goulão · NeuralShift · Portugal
4
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2
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f55cb50f0b282372, topic:reinforcement-learning, topic:gymnasium, desc:reinforcement learning
matched fp:f55cb50f0b282372, topic:datasets, readme:dataset, desc:datasets