From Explicit Communication To Tacit Cooperation:a Novel Paradigm For Cooperative MARL
2023 Β· Dapeng Li, Zhiwei Xu, Bin Zhang, et al.
Abstract
Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks. However, partial observability issues and the absence of effectively shared signals between agents often limit its effectiveness in fostering cooperation. While communication can address this challenge, it simultaneously reduces the algorithm's practicality. Drawing inspiration from human team cooperative learning, we propose a novel paradigm that facilitates a gradual shift from explicit communication to tacit cooperation. In the initial training stage, we promote cooperation by sharing relevant information among agents and concurrently reconstructing this information using each agent's local trajectory. We then combine the explicitly communicated information with the reconstructed information to obtain mixed information. Throughout the training process, we progressively reduce the proportion of explicitly communicated information, facilit
Authors
(none)
Tags
Stats
Related papers
- Tacit Learning With Adaptive Information Selection For Cooperative Multi-agent Reinforcement Learning (2024)0.00
- Is Centralized Training With Decentralized Execution Framework Centralized Enough For MARL? (2023)0.00
- An Initial Introduction To Cooperative Multi-agent Reinforcement Learning (2024)0.00
- Contextual Knowledge Sharing In Multi-agent Reinforcement Learning With Decentralized Communication And Coordination (2025)0.00
- CTDS: Centralized Teacher With Decentralized Student For Multi-agent Reinforcement Learning (2022)0.00
- Bridging Training And Execution Via Dynamic Directed Graph-based Communication In Cooperative Multi-agent Systems (2024)0.00
- TACTIC: Task-agnostic Contrastive Pre-training For Inter-agent Communication (2025)3.58
- Cautiously-optimistic Knowledge Sharing For Cooperative Multi-agent Reinforcement Learning (2023)5.84