MASQ: Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914976637124608 |
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| author | Liu, Qi Guo, Jingxiang Lin, Sixu Ma, Shuaikang Zhu, Jinxuan Li, Yanjie |
| author_facet | Liu, Qi Guo, Jingxiang Lin, Sixu Ma, Shuaikang Zhu, Jinxuan Li, Yanjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MASQ: Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion Liu, Qi Guo, Jingxiang Lin, Sixu Ma, Shuaikang Zhu, Jinxuan Li, Yanjie Robotics 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. |
| title | MASQ: Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.13759 |