HIVEX: A High-Impact Environment Suite for Multi-Agent Research

Philipp Dominic Siedler

Keywords: Environment Benchmark, Multi-Agent Reinforcement Learning, Multi-Agent Systems, Critical Ecological Challenges

TL;DR: A suite of multi-agent environments aimed at real-world critical ecological challenges, offering benchmarks for advancing research in areas like wildfire management, ocean plastic collection, and drone-based reforestation.

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.

Primary Area: datasets and benchmarks

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Submission Number: 10232