A deep reinforcement learning method for multi-heterogeneous robot systems based on global status

Yanjiang Chen; Junqin Lin; Zhiyuan Yu; Zaiping Zheng; ChunYu Fu; Kui Huang

doi:10.1117/12.3026373

27 March 2024 A deep reinforcement learning method for multi-heterogeneous robot systems based on global status

Yanjiang Chen, Junqin Lin, Zhiyuan Yu, Zaiping Zheng, ChunYu Fu, Kui Huang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131053I (2024) https://doi.org/10.1117/12.3026373
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

At present, multi-agent reinforcement learning(MARL) is in the initial stage of development, and research on strongly coupled multi-agent reinforcement learning is not in-depth. We propose a deep reinforcement learning network architecture for multi-heterogeneous robot systems based on a global status simulation platform. This architecture is based on clipped PPO and constructs a strategy model by constructing the full state action space and full action space of multi-heterogeneous robots. We trained and tested the algorithm on a multi-heterogeneous robot simulation platform, and the results showed that the proposed method can effectively target artificially fixed strategies and achieve victory.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanjiang Chen, Junqin Lin, Zhiyuan Yu, Zaiping Zheng, ChunYu Fu, and Kui Huang "A deep reinforcement learning method for multi-heterogeneous robot systems based on global status", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131053I (27 March 2024); https://doi.org/10.1117/12.3026373

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