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.

翻译：游戏已成为基于智能体研究快速发展的关键测试平台。过去已取得显著进展，但尚不清楚这些发现是否适用于现实世界问题。随着压力日益增长，一些最严峻的生态挑战可通过技术及其应用找到缓解与预防方案。大多数现实领域都包含多智能体场景，需要机器-机器及人-机协作。开源环境发展滞后，常局限于玩具场景，过于抽象或不适用于多智能体研究。通过模拟现实问题并提升环境复杂度，我们期望推动前沿多智能体研究，激励研究者致力于解决紧迫的现实问题。本文提出HIVEX——一个专注于生态挑战的多智能体研究基准测试环境套件。HIVEX包含以下环境：风电场控制、野火资源管理、无人机辅助再造林、海洋塑料收集与空中野火扑救。我们为主任务及子任务提供环境配置、训练示例与基线模型。本实验所有训练模型均托管于Hugging Face平台。我们同步在Hugging Face设立排行榜，并鼓励社区提交基于本环境套件训练的模型。