gcaa 1.0.0

Dynamic decentralized task allocation algorithms for multi-agent systems using a greedy auction algorithm

Task Allocation using Auctions

Dynamic decentralized task allocation algorithms for multi-agent systems using a greedy auction algorithm. It's available in Matlab and Python.

Official GitHub repository: https://github.com/MartinBraquet/task-allocation-auctions.

Master's research at The University of Texas at Austin in the research group of Efstathios Bakolas.

The paper resulting from these simulations has been published at the Modeling, Estimation, and Control Conference (MECC 2021).

To cite this work:

Braquet, M. and Bakolas E., "Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems", Modeling, Estimation and Control Conference (MECC), 2021.

Official paper link: https://doi.org/10.1016/j.ifacol.2021.11.249.

Demo

2D map of the dynamic task allocation (10 agents and 10 tasks) with associated reward, cost, and utility

With limited communication:

With free communication:

Matlab

In the matlab folder.

• For the dynamic task allocation, run OptimalControl_DTA.m.
• For the sensitivity analysis of the parameters, run optimalControlParametersAnalysis.m.

To run the code in Matlab online: https://drive.matlab.com/sharing/f36a058f-99a4-4e38-a08e-0af800bd4ce8.

Python

In the gcaa folder.

Installation

The Python package works on any major OS (Linux, Windows, and macOS) and with Python >= 3.11.

The most straightforward way is to simply install it from PyPI via:

pip install gcaa

If you want to install it from source, which is necessary for development, follow the instructions here.

If some dependencies release changes that break the code, you can install the project from its lock file—which fixes the dependency versions to ensure reproducibility:

pip install -r requirements.txt

Usage

For the dynamic task allocation, run:

import gcaa
gcaa.optimal_control_dta(
    nt=4, # number of tasks
    na=5, # number of agents
    uniform_agents=False, # whether agents have an initial speed
    n_rounds=20, # number of simulation rounds (precision vs compute time)
    limited_communication=True, # whether communication is limited (True, False, or 'both')
)

The sensitivity analysis of the parameters isn't available in Python.

Tests

pytest gcaa

Feedback

For any issue / bug report / feature request, open an issue.

Contributions

To provide upgrades or fixes, open a pull request.