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 (CCKS) framework, which allows agents to adopt recommendations based on consensus-derived constraints and to follow the teacher's instructions more smartly. This mechanism enables agents to balance exploration and learning from experienced teachers, improving overall performance. The key is the consensus model construction, for which we propose to employ contrastive learning to construct consensus models based on local observations in the agents' training phase. In action selection, agents score and choose actions based on consensus and shared knowledge. Designed as a plug-and-play solution, CCKS integrates seamlessly with existing DTDE algorithms. Experiments conducted in the Google Research Football environment and the complex StarCraft II Multi-Agent Challenge demonstrate that the integration with CCKS significantly improves cooperation efficiency, learning speed, and overall performance compared with current DTDE baselines. The code is available at https://github.com/yuanxpy/CCKS.

翻译：在面向合作型多智能体强化学习的分散训练与分散执行（DTDE）范式中，基于行动建议的知识共享机制促进了智能体间的可解释性与可扩展性协作。然而，当前的行动建议方法往往过度遵循教师指导而忽视师生兼容性评估，导致过度建议、稳定性欠佳及性能退化等问题。为应对这些挑战，本文提出基于共识的通信与知识共享（CCKS）框架，该框架允许智能体依据共识衍生的约束采纳建议，并更智能地遵循教师指导。该机制使智能体能够在探索与向经验丰富的教师学习之间取得平衡，从而提升整体性能。其关键在于共识模型的构建，为此我们提出在智能体训练阶段利用对比学习基于局部观测构建共识模型。在行动选择过程中，智能体依据共识与共享知识对行动进行评分与筛选。作为即插即用的解决方案，CCKS可与现有DTDE算法无缝集成。在谷歌研究足球环境与复杂的星际争霸II多智能体挑战实验表明，与现有DTDE基线相比，集成CCKS可显著提升协作效率、学习速度与整体性能。相关代码已开源至https://github.com/yuanxpy/CCKS。