arXiv 2408.137592024-10-17cs.RO

MASQ：用于单体四足机器人运动的多智能体强化学习

MASQ: Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion

Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；School of Mechanical Engineering and Automation（机械工程与自动化学院）；Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics（广东省智能变胞机构与自适应机器人重点实验室）

Qi Liu, Jingxiang Guo, Sixu Lin, Shuaikang Ma, Jinxuan Zhu, Yanjie Li

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AI总结：

本文提出MASQ方法，将单体四足机器人的每条腿视为智能体，通过共享全局评论家的多智能体强化学习进行协作探索，显著提升了运动学习的收敛速度与真实环境中的鲁棒性。

摘要：

本文提出了一种利用多智能体深度强化学习（MARL）来改进单体四足机器人运动学习的新方法。许多现有方法对单个机器人使用单智能体强化学习，或对多机器人系统的协作任务使用MARL。与现有方法不同，本文提出将MARL用于单体四足机器人的运动学习。我们开发了一种名为MASQ的学习结构，将每条腿视为一个智能体来探索四足机器人的动作空间，共享一个全局评论家，并进行协作学习。实验结果表明，MASQ不仅加快了学习收敛速度，还增强了在真实环境中的鲁棒性，这表明将MASQ应用于四足等单体机器人可能超越传统的单机器人强化学习方法。我们的研究为将MARL与单体机器人运动学习相结合提供了深刻的指导。

英文摘要：

This paper proposes a novel method to improve locomotion learning for a single quadruped robot using multi-agent deep reinforcement learning (MARL). Many existing methods use single-agent reinforcement learning for an individual robot or MARL for the cooperative task in multi-robot systems. Unlike existing methods, this paper proposes using MARL for the locomotion learning of a single quadruped robot. We develop a learning structure called Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion (MASQ), considering each leg as an agent to explore the action space of the quadruped robot, sharing a global critic, and learning collaboratively. Experimental results indicate that MASQ not only speeds up learning convergence but also enhances robustness in real-world settings, suggesting that applying MASQ to single robots such as quadrupeds could surpass traditional single-robot reinforcement learning approaches. Our study provides insightful guidance on integrating MARL with single-robot locomotion learning.

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