Metadata-Version: 2.4
Name: bluebird-gymnasium
Version: 0.2.0
Summary: An gymnasium environment for air traffic control.
Project-URL: Documentation, http://docs.projectbluebird.ai
Project-URL: Repository, https://github.com/project-bluebird/BluebirdATC
Author: Project Bluebird
License-Expression: AGPL-3.0-or-later
Requires-Python: <3.14,>=3.10
Requires-Dist: bluebird-dt==0.2.0
Requires-Dist: gymnasium>=0.29.1
Requires-Dist: imageio<3,>=2.37.3
Requires-Dist: jupyter<2,>=1.1.1
Description-Content-Type: text/markdown

# Bluebird Gymnasium

A suite of gymnasium environments for air traffic control (ATC).
The environments are based on [bluebird-dt](https://github.com/project-bluebird/BluebirdATC/tree/main/bluebird-dt) (an ATC simulator).

The environments support research in agent-based learning (e.g. reinforcement learning) for ATC.
It supports either single agent or multi-agents scenarios.

## Installation

`bluebird-gymnasium` is available on pypi, therefore it can be installed using

```
pip install bluebird-gymnasium
```

or, if using [UV](https://docs.astral.sh/uv/), you can add it to your environment using
```
uv add bluebird-gymnasium
```

## Getting started

### Basic usage

bluebird-gymnasium currently supports the following environments/airspace:
X sector, Y sector, I sector, Xplus sector and Springfield sector.

To instantiate a X sector environment with the default config, run:

```python
import gymnasium as gym
import bluebird_gymnasium
env = gym.make("SectorXEnv-v0")
```

### Sample agents

Below, an example agent that takes random actions.

```python
import gymnasium as gym
import bluebird_gymnasium

env = gym.make("SectorXEnv-v0")
obs, info = env.reset()
done = False

while not done:
    action = env.action_space.sample()
    obs, reward, done, truncated, info = env.step(action)
```

## Documentation

The documentation of the latest release is available at [https://docs.projectbluebird.ai](https://docs.projectbluebird.ai).
