标题： MASH：用于单个类人机器人移动的协作异构多智能体强化学习 显示英文标题

标题： MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion

摘要： 本文提出了一种新方法，通过合作异构多智能体深度强化学习（MARL）来增强单个人形机器人的运动能力。 尽管大多数现有方法通常为单个人形机器人使用单智能体强化学习算法或为多机器人系统任务使用MARL算法，我们提出了一种不同的范式：将合作异构MARL应用于优化单个人形机器人的运动能力。 所提出的方法，单人形机器人运动的多智能体强化学习（MASH），将每个肢体（腿和臂）视为一个独立的智能体，在探索机器人动作空间的同时共享一个全局评论家以进行合作学习。 实验表明，MASH加速了训练收敛并提高了全身协作能力，优于传统的单智能体强化学习方法。 这项工作推进了MARL在单人形机器人控制中的集成，为高效的运动策略提供了新的见解。 显示英文摘要

摘要： This paper proposes a novel method to enhance locomotion for a single humanoid robot through cooperative-heterogeneous multi-agent deep reinforcement learning (MARL). While most existing methods typically employ single-agent reinforcement learning algorithms for a single humanoid robot or MARL algorithms for multi-robot system tasks, we propose a distinct paradigm: applying cooperative-heterogeneous MARL to optimize locomotion for a single humanoid robot. The proposed method, multi-agent reinforcement learning for single humanoid locomotion (MASH), treats each limb (legs and arms) as an independent agent that explores the robot's action space while sharing a global critic for cooperative learning. Experiments demonstrate that MASH accelerates training convergence and improves whole-body cooperation ability, outperforming conventional single-agent reinforcement learning methods. This work advances the integration of MARL into single-humanoid-robot control, offering new insights into efficient locomotion strategies.