研究人员推出CCKS，一个旨在增强去中心化多智能体强化学习中通信和知识共享的框架。这种新方法通过使智能体能够基于共识提出建议并智能地遵循教师指令，解决了当前动作建议方法的局限性。在Google Research Football和StarCraft II等环境中的实验表明，CCKS提高了合作、学习速度和整体性能。 AI
影响 增强了去中心化多智能体系统中的合作和学习速度，有望提高复杂模拟中的性能。
排序理由 这是一篇描述多智能体强化学习新框架的研究论文。
在 arXiv cs.MA (Multiagent) 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
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,…
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 t…