将多智能体强化学习用于单人形机器人行走，提升运动协调性与训练效率。
MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion
- 每条肢体作为独立智能体，共享全局评判器协同优化
- 训练收敛速度更快，全身协作能力显著提升
- 适合研究人形机器人运动控制与多智能体协同的新思路
本文提出一种新方法——针对单一人形机器人行走的多智能体强化学习（MASH），通过将每个肢体（腿和臂）视为独立智能体，在共享全局评判器的条件下探索动作空间。与传统单智能体强化学习相比，该方法显著加速了训练收敛，并提升了整体身体的协作能力。实验表明，该框架有效增强了单一人形机器人的运动协调性，推动了多智能体强化学习在单机器人控制中的应用，为高效运动策略设计提供了新范式。
原文摘要 · Abstract (English)
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.
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