HIVEX: A High-Impact Environment Suite for Multi-Agent Research (extended version)

TL;DR

HIVEX is a comprehensive suite of realistic, complex multi-agent environments designed to advance research on ecological challenges, providing benchmarks, training tools, and a community platform to foster real-world applications.

Contribution

The paper introduces HIVEX, a new open-source environment suite with diverse ecological scenarios to improve multi-agent research relevance and impact.

Findings

HIVEX includes five complex ecological environments.

Models trained on HIVEX are hosted and benchmarked on Hugging Face.

A community leaderboard encourages ongoing research and model submissions.

Abstract

Games have been vital test beds for the rapid development of Agent-based research. Remarkable progress has been achieved in the past, but it is unclear if the findings equip for real-world problems. While pressure grows, some of the most critical ecological challenges can find mitigation and prevention solutions through technology and its applications. Most real-world domains include multi-agent scenarios and require machine-machine and human-machine collaboration. Open-source environments have not advanced and are often toy scenarios, too abstract or not suitable for multi-agent research. By mimicking real-world problems and increasing the complexity of environments, we hope to advance state-of-the-art multi-agent research and inspire researchers to work on immediate real-world problems. Here, we present HIVEX, an environment suite to benchmark multi-agent research focusing on ecological challenges. HIVEX includes the following environments: Wind Farm Control, Wildfire Resource Management, Drone-Based Reforestation, Ocean Plastic Collection, and Aerial Wildfire Suppression. We provide environments, training examples, and baselines for the main and sub-tasks. All trained models resulting from the experiments of this work are hosted on Hugging Face. We also provide a leaderboard on Hugging Face and encourage the community to submit models trained on our environment suite.