Prism: Spectral Parameter Sharing for Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2602.06476v1
• Date: Fri, 06 Feb 2026 08:05:11 GMT
• Title: Prism: Spectral Parameter Sharing for Multi-Agent Reinforcement Learning
• Authors: Kyungbeom Kim, Seungwon Oh, Kyung-Joong Kim,
• Abstract summary: We propose Prism, a parameter sharing framework that induces inter-agent diversity by representing shared networks in the spectral domain via singular value decomposition (SVD)<n>Experiments on both homogeneous (LBF, SMACv2) and heterogeneous benchmarks show that Prism achieves competitive performance with superior resource efficiency.
• Score: 2.504298819189614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter sharing is a key strategy in multi-agent reinforcement learning (MARL) for improving scalability, yet conventional fully shared architectures often collapse into homogeneous behaviors. Recent methods introduce diversity through clustering, pruning, or masking, but typically compromise resource efficiency. We propose Prism, a parameter sharing framework that induces inter-agent diversity by representing shared networks in the spectral domain via singular value decomposition (SVD). All agents share the singular vector directions while learning distinct spectral masks on singular values. This mechanism encourages inter-agent diversity and preserves scalability. Extensive experiments on both homogeneous (LBF, SMACv2) and heterogeneous (MaMuJoCo) benchmarks show that Prism achieves competitive performance with superior resource efficiency.

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