mlsanityData to 14 Oct 2026

CCKS: Consensus-based Communication and Knowledge Sharing

arXiv 2606.12281 ↗First posted 10 Jun 2026cs.MA cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 23 references that are in the map. First posted on arXiv 10 Jun 2026.

Builds on

17 of 23 references.

References · 17

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2019The StarCraft Multi-Agent Challengecited by 597
  3. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  4. 2016Learning Multiagent Communication with Backpropagationcited by 366
  5. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 261
  6. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  7. 2015Multiagent Cooperation and Competition with Deep Reinforcement Learningcited by 184
  8. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  9. 2019Mastering Complex Control in MOBA Games with Deep Reinforcement Learningcited by 161
  10. 2020Learning Individually Inferred Communication for Multi-Agent Cooperationcited by 55
  11. 2019PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learningcited by 47
  12. 2022Contrastive Identity-Aware Learning for Multi-Agent Value Decompositioncited by 19
5 more
  1. 2021HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messagingcited by 13
  2. 2022Consensus Learning for Cooperative Multi-Agent Reinforcement Learningcited by 8
  3. 2020A Q-values Sharing Framework for Multiagent Reinforcement Learning under Budget Constraintcited by 7
  4. 2023Cautiously-Optimistic Knowledge Sharing for Cooperative Multi-Agent Reinforcement Learningcited by 5
  5. 2022Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learningcited by 4

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  1. 2024Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
  2. 2025Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination
  3. 2024An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning
  4. 2025Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
  5. 2026Interactive Distillation for Cooperative Multi-Agent Reinforcement Learning
  6. 2025Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learning
  7. 2024An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
  8. 2024Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey