2026· International Conference on Conceptual Structures· pp. 57-71· 0 citations· 13 references
Computer Science
TL;DR
Experimental results demonstrate that model-based, decentralized approach can serve as an effective alternative to centralized training for cooperative multi-agent reinforcement learning and indicate that model-based, decentralized approach can serve as an effective alternative to centralized training for cooperative multi-agent reinforcement learning.
M3W is a novel approach that applies mixture-of-experts (MoE) to world model instead of policy, enabling both learning and planning, and demonstrates superior performance, sample efficiency, and multi-task adaptability.
Zi-Jie Zhao, Zhong Zhao, Kaixuan Xu et al.· Neural Information Processin...· 9 citations
Multi-agent reinforcement learning (MARL) is a powerful paradigm for large-scale collaborative scenarios, yet it is often hampered by partial observability and non-stationarity. While communication can alleviate these issues, designing efficient protocols remains a significant challenge, especially in decentralized settings. Many existing methods suffer from high communication volume and training complexity. To overcome these limitations, we propose CSN, which enables efficient Communication with Skill Neurons in decentralized MARL by exchanging the essential components of learned knowledge at neuron level. Specifically, we first identify skill neurons, a subset of neurons that encode the most critical knowledge acquired during local training. By communicating only a sparse subset of model parameters and doing so intermittently, CSN fundamentally reduces communication volume by avoiding redundant information and frequent communication. Extensive experiments on the SMAC, SMACv2, MPE and Predator-Prey benchmarks validate that CSN significantly outperforms state-of-the-art methods in both performance and communication efficiency, verifying its effectiveness in decentralized MARL.
Jiahua Lan, Li Shen, Ruijun Liu et al.· IEEE Transactions on Pattern...· 0 citations
This paper develops a multi-agent reinforcement learning-based (MARL) delegation training that enables agents to make sequential delegation decisions while minimizing the total execution cost and introduces two new frameworks for collaboration and delegation in multi-agent systems.
Ziqing Lu, Avinash Mudireddy, Sarra M. Alqahtani et al.· 0 citations
Action Generation with Topology Awareness (AGTA), a topology-aware sequential decision-making framework in MARL that integrates inter-agent correlation modeling with topology-guided decision-order optimization, and outperforms the state-of-the-art counterparts.
Kun Hu, Shanghua Wen, Wendi Wu et al.· Mathematics· 0 citations
This work applies multi-agent actor-critic and multi-agent attention-actor-critic approaches – off-policy multi-agent re-inforcement learning (MARL) approaches – in the MARL imitation learning inner loop, as opposed to MACK – the on-policy MARL method used in MAGAIL.
Wonseok Jeon, Paul Barde, Joelle Pineau et al.· 2 citations
This paper presents a narrative survey of recent developments in MARL and examines research directions centred on centralised training with decentralised execution (CTDE), value decomposition, learned communication, graph-based methods, and model-based learning.
Abdur Rakib, K. Phung, Marco Pérez Hernández et al.· Applied Sciences· 0 citations
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