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Description:
Toolbox for testing and evaluating multi-agent control systems on two warehouse-inspired environments: Multi-agent Pick Up And Delivery (MAPD), Multi-agent Pathfinding (MAPF). It can be used to benchmark the performance of several multi-agent planning and learning methods.

More Information:
<ul>
<li>Grand Challenge/Workpackage(s): <a href="https://www.flandersairesearch.be/en/research/research-challenges/collaborative-ai" target="_blank" rel="noopener">GC3</a> - WP2</li>
<li>Responsible Research Lead: Ann Now&eacute;</li>
<li>Authors: Marjon Blondeel</li>
<li>Programming Language: C#/Python</li>
<li>Link to the website: <a href="https://ai.vub.ac.be/toolbox-multi-agent-systems/" target="_blank" rel="noopener">https://ai.vub.ac.be/toolbox-multi-agent-systems/</a></li>
<li>Link to the repository: MAPD (<a href="https://gitlab.ai.vub.ac.be/multi-agent-benchmarking/pick-up-and-delivery" target="_blank" rel="noopener">https://gitlab.ai.vub.ac.be/multi-agent-benchmarking/pick-up-and-delivery</a>), MAPF (<a href="https://gitlab.ai.vub.ac.be/multi-agent-benchmarking/path-finding" target="_blank" rel="noopener">https://gitlab.ai.vub.ac.be/multi-agent-benchmarking/path-finding</a>)</li>
</ul>

Toolbox containing several MAS environments, allowing to compare different algorithms