发表机构
University of Cambridge; Carnegie Mellon University; MorphoAI; Technical University of Munich（剑桥大学; 卡内基梅隆大学; MorphoAI; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出协同设计健身房基准，涵盖20个领域、85种预设，用于联合优化智能体体现与策略，并系统评估现有算法，推动该领域研究。
AI 中文摘要
在给定环境中寻找最优行为策略是一个在游戏、机器人、能源基础设施、通信网络和多智能体系统等不同领域广泛研究的问题。为支持此类研究，已开发出众多基准，但绝大多数假设智能体的体现（设计）是固定的，仅专注于策略学习。放宽这一假设会引发一类更广泛的问题，其中分别优化体现和策略是高度次优的。智能体的体现强烈影响哪些控制策略可以被发现，而最优体现又由其允许的策略所定义。为了帮助研究社区明确且系统地研究这类问题，我们引入了协同设计健身房——一套用于联合优化体现和策略的基准环境套件。我们的环境涵盖机器人操作和移动、多机器人协作、可变形和软体动力学、视频游戏、电网、无线网络、F1赛车、多智能体仓库和最优控制等领域，提供20个环境家族（领域），总计超过85个不同的协同设计预设。我们进一步对代表性协同设计算法进行了系统评估，刻画了当前最先进水平。这些贡献共同为协同设计中累积性、可比较的进展奠定了基础。
英文摘要
Finding an optimal behaviour policy within a given environment is a widely studied problem in domains as diverse as games, robotics, energy infrastructure, communication networks, and multi-agent systems. Numerous benchmarks have been developed to support such research, but the vast majority assume that the agent's embodiment (design) is fixed, focusing instead on policy learning alone. Lifting this assumption gives rise to a broader class of problems in which optimizing embodiment and policy separately is highly suboptimal. An agent's embodiment strongly shapes which control policies can be discovered, while the optimal embodiment is in turn defined by the policies it admits. To help the research community study this class of problems explicitly and systematically, we introduce Co-Design Gym - a suite of benchmark environments for jointly optimizing embodiment and policy. Our environments span domains such as robotic manipulation and locomotion, multi-robot cooperation, deformable and soft dynamics, video games, electricity grids, wireless networks, F1 racing, multi-agent warehouses, and optimal control, offering 20 environment families (domains), with over 85 distinct co-design presets in total. We further contribute a systematic evaluation of representative co-design algorithms, characterizing the current state of the art. Together, these contributions lay the groundwork for cumulative, comparable progress in co-design.
CommentsAviraj Newatia and Yordan Tsvetkov contributed equally
