bluebird-gymnasium 0.2.0

An gymnasium environment for air traffic control.

Bluebird Gymnasium

A suite of gymnasium environments for air traffic control (ATC). The environments are based on 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, 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:

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

Sample agents

Below, an example agent that takes random actions.

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.