Effects of Spectral Normalization in Multi-agent Reinforcement Learning

2022/12/10 by Kinal Mehta, Mehta, Kinal, Anuj Mahajan +3 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.2212.05331

openalex publication_date 2022/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1) The joint action space grows exponentially with the number of agents 2) This, combined with the reward sparseness and environment noise, leads to large sample requirements for accurate learning. We show that regularising the critic with spectral normalization (SN) enables it to learn more robustly, even in multi-agent on-policy sparse reward scenarios. Our experiments show that the regularised critic is quickly able to learn from the sparse rewarding experience in the complex SMAC and RWARE domains. These findings highlight the importance of regularisation in the critic for stable learning.

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• Multi-Agent Guided Policy Search for Non-Cooperative Dynamic Games
• Unifying Agent Interaction and World Information for Multi-agent Coordination

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