multigrid

KO | EN

gitlite — search

#javascript #react #llm #go #rust #linux #docker #macos #java #cli #vue #security #mcp #open-source

(★ 50)

Fast and flexible multi-agent gridworld reinforcement learning environments.

multigrid Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# MultiGrid

The **MultiGrid** library provides contains a collection of fast multi-agent discrete gridworld environments for reinforcement learning in [Gymnasium](https://github.com/Farama-Foundation/Gymnasium). This is a multi-agent extension of the [minigrid](https://github.com/Farama-Foundation/Minigrid) library, and the interface is designed to be as similar as possible.

The environments are designed to be fast and easily customizable. Compared to minigrid, the underlying gridworld logic is **significantly optimized**, with environment simulation 10x to 20x faster by our benchmarks.

Documentation for this library can be found at [ini.github.io/docs/multigrid](https://ini.github.io/docs/multigrid).

## Installation

pip install multigrid

Or alternatively, for an editable install:

git clone https://github.com/ini/multigrid
    cd multigrid
    pip install -e .

This package requires Python 3.9 or later.

## Environments

The `multigrid.envs` package provides implementations of several multi-agent environments. [You can find the full list here](https://ini.github.io/docs/multigrid/multigrid/multigrid.envs).

## API

MultiGrid follows the same pattern as RLlib's [MultiAgentEnv API](https://docs.ray.io/en/latest/rllib/rllib-env.html#multi-agent-and-hierarchical) and PettingZoo's [ParallelEnv API](https://pettingzoo.farama.org/api/parallel/).

```python
import gymnasium as gym
import multigrid.envs

env = gym.make('MultiGrid-Empty-8x8-v0', agents=2, render_mode='human')

observations, infos = env.reset()
while not env.unwrapped.is_done():
   # this is where you would insert your policy / policies
   actions = {agent.index: agent.action_space.sample() for agent in env.unwrapped.agents}
   observations, rewards, terminations, truncations, infos = env.step(actions)

env.close()
```

More information about using MultiGrid directly with other APIs:
* [PettingZoo](https://ini.github.io/docs/multigrid/multigrid/multigrid.pettingzoo)
* [RLlib](https://ini.github.io/docs/multigrid/multigrid/multigrid.rllib)

## Training Agents

See the [scripts folder](./scripts) for an example training with RLlib.

## Documentation

Documentation for this package can be found at [ini.github.io/docs/multigrid](https://ini.github.io/docs/multigrid).

## Citation

To cite this project please use:

```
@article{oguntola2023theory,
  title={Theory of mind as intrinsic motivation for multi-agent reinforcement learning},
  author={Oguntola, Ini and Campbell, Joseph and Stepputtis, Simon and Sycara, Katia},
  journal={arXiv preprint arXiv:2307.01158},
  year={2023}
}
```

multigrid

multigrid Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

multigrid

multigrid Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README