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
CSN is proposed, which enables efficient Communication with Skill Neurons in decentralized MARL by exchanging the essential components of learned knowledge at neuron level by communicating only a sparse subset of model parameters and doing so intermittently.
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, Wen-Di 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, Khoa Phung, Marco Pérez Hernández et al.· Applied Sciences· 0 citations
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