基于角色的自适应参数共享方法

doi:10.16451/j.cnki.issn1003-6059.202503001

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Abstract In large-scale heterogeneous multi-agent reinforcement learning, parameter sharing is often utilized to reduce the number of training parameters and accelerate the training process. However, the traditional full parameter sharing approach is prone to causing excessive behavioral uniformity among agents, while independent parameter training methods are constrained by computational complexity and memory limitations. Therefore, a role-based adaptive parameter sharing(RAPS) method is proposed in this paper. First, agents are grouped into roles based on their task characteristics. Then, within a unified network structure, sparse sub-network structures are generated for different agent roles by integrating unstructured network pruning techniques. A dynamic adjustment mechanism is introduced to adaptively optimize the ratio of shared and independent parameters according to task requirements. Additionally, a collaborative loss function between roles is incorporated to further enhance coordination among heterogeneous agents. Thus, computational complexity is effectively reduced by RAPS while behavioral diversity among heterogeneous agents is preserved. Experimental results demonstrate that RAPS improves the performance and scalability of multi-agent systems significantly in different multi-agent tasks.

Key words： Large-Scale Heterogeneous Multi-agent Reinforcement Learning Parameter Sharing Unstructured Network Pruning Role Grouping

Received: 16 January 2025

ZTFLH:

TP391

Fund:Natural Science Foundation of Anhui Province(No.2308085MF203), University Synergy Innovation Program of Anhui Province(No.GXXT-2022-055), Open Fund of Key Laboratory of Flight Techniques and Flight Safety, CAAC(No.FZ2022KF09), R&D Program of Key Laboratory of Flight Techniques and Flight Safety, CAAC(No.FZ2022ZZ02)

Corresponding Authors: FANG Baofu, Ph.D., associate professor. His research interests include intelligent robot systems.

About author:: WANG Qiong, Master student. Her research interests include multi-agent deep reinforcement learning.
WANG Hao, Ph.D., professor. His research interests include distributed intelligent systems and robots.
WANG Zaijun, Master, researcher. Her research interests include multi-robot task allo-cation and artificial intelligence.

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Cite this article:

FANG Baofu,WANG Qiong,WANG Hao等. Role-Based Adaptive Parameter Sharing Method[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(3): 193-204.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202503001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I3/193

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