New Framework Improves Multi-Agent Reinforcement Learning Through Consensus-Based Knowledge Sharing
Researchers have developed CCKS, a framework that enables artificial agents in decentralized systems to more intelligently adopt recommendations from experienced teachers by using consensus-derived constraints. The approach addresses limitations in current action-advising methods that cause agents to over-rely on teacher guidance without evaluating compatibility. The framework shows improved cooperation efficiency and learning speed in complex multi-agent environments like Google Research Football and StarCraft II.
A new paper on arXiv presents CCKS (Consensus-based Communication and Knowledge Sharing), a framework designed to improve cooperation among multiple artificial agents learning together in decentralized systems. The core innovation addresses a problem in current approaches where agents blindly follow teacher guidance without evaluating whether the teacher's advice is actually suitable for their specific situation, leading to poor performance and instability. CCKS uses contrastive learning to build consensus models based on local observations, allowing agents to score and selectively adopt recommendations rather than accepting all guidance. The framework is designed as a plug-and-play solution that works with existing decentralized training and execution algorithms. Experiments in complex environments—Google Research Football and StarCraft II Multi-Agent Challenge—demonstrate that integrating CCKS significantly improves cooperation efficiency, learning speed, and overall performance compared to baseline approaches.
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- arXiv cs.AICenter
CCKS: Consensus-based Communication and Knowledge Sharing
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