FANG Baofu1,2, WANG Qiong1, WANG Hao1, WANG Zaijun3
1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601; 2. College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052; 3. Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan 618307
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.
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2025, Vol. 38 
