New Framework Improves Multi-Agent Reinforcement Learning Through Consensus-Based Knowledge Sharing
Researchers have proposed CCKS, a consensus-based communication and knowledge sharing framework for cooperative multi-agent reinforcement learning that helps AI agents selectively follow peer guidance. Current action-advising approaches suffer from over-reliance on teacher agents, leading to excessive advising and degraded performance. CCKS addresses this by using contrastive learning to build consensus models, enabling smarter, more balanced learning across agents.
A new framework called Consensus-based Communication and Knowledge Sharing (CCKS) has been introduced to improve cooperation in Decentralized Training and Decentralized Execution (DTDE) multi-agent reinforcement learning systems. The core problem it targets is that existing action-advising methods cause agents to follow teacher guidance too rigidly, without assessing whether the teacher and student agents are actually compatible in their situations. CCKS uses contrastive learning to construct consensus models from local observations during training, allowing agents to score and select actions based on both consensus constraints and shared knowledge. The framework is designed as a plug-and-play module, meaning it can be integrated with existing DTDE algorithms without requiring architectural overhauls. Experiments in the Google Research Football environment and the StarCraft II Multi-Agent Challenge showed that CCKS improved cooperation efficiency, learning speed, and overall performance compared to current baselines. The code has been made publicly available by the authors.
What's missing
The paper does not report results on environments beyond Google Research Football and StarCraft II Multi-Agent Challenge, leaving open questions about generalizability to other cooperative MARL domains. The work has not yet undergone peer review, as it is a preprint.
What different sources said
- arXiv cs.AICenter
CCKS: Consensus-based Communication and Knowledge Sharing
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