HIVEX构建真实生态问题多智能体环境，推动技术解决现实挑战。
HIVEX: A High-Impact Environment Suite for Multi-Agent Research (extended version)
- 构建五个真实生态场景的多智能体仿真环境，模拟风场控制、火灾管理等复杂任务。
- 提供训练示例、基线模型与排行榜，支持社区协作评测与模型提交。
- 聚焦真实世界问题，适合关注可持续发展与多智能体协同的研究者。
游戏作为智能体研究的重要测试平台，虽取得显著进展，但其成果是否适用于真实世界仍不明确。随着生态挑战日益严峻，技术应用有望提供缓解与预防方案。多数现实场景涉及多智能体协作，需机器与机器、人与机器共同参与。现有开源环境多为简化玩具场景，抽象或不适用于多智能体研究。为此，我们提出HIVEX——一套聚焦生态问题的多智能体基准环境套件，包含风力发电场调控、野火资源管理、无人机植树、海洋塑料收集和空中灭火五项任务。提供完整环境、训练示例、子任务基线模型，所有实验模型已部署于Hugging Face，并设立排行榜，鼓励社区提交模型以推动研究进展。
原文摘要 · Abstract (English)
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.
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