arXiv:2606.12281v1 Announce Type: cross Abstract: In Decentralized Training and Decentralized Execution (DTDE) for cooperative Multi-Agent Reinforcement Learning (MARL), action-advising-based knowledge sharing promotes interpretable and scalable cooperation among agents. However, current action advising approaches often adhere too much to the teacher's guidance without evaluating teacher-student compatibility, which causes excessive advising, suboptimal stability, and degraded performance. To overcome these challenges, this paper presents a Consensus-based Communication and Knowledge Sharing (
The rapid advancement in Multi-Agent Reinforcement Learning necessitates more sophisticated communication and knowledge-sharing protocols to improve performance and stability.
Improving decentralized training and execution in MARL is crucial for developing robust and scalable AI systems, directly influencing AI's deployment in complex, real-world environments.
The proposed Consensus-based Communication and Knowledge Sharing (CCKS) method could lead to more efficient and reliable multi-agent systems by addressing limitations of current action advising approaches.
- · AI developers
- · Robotics
- · Logistics optimization
- · Decentralized AI applications
- · Inefficient multi-agent systems
- · Current action advising approaches
Enhances the ability of AI agents to cooperate effectively and learn from each other in complex tasks.
Accelerates the development and deployment of autonomous systems in diverse sectors, from defense to advanced manufacturing.
Potentially enables new forms of collaborative AI that can operate with greater autonomy and less human oversight, impacting white-collar workflows.
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