Jul 2026· CCF Transactions on Pervasive Computing and Interaction· Vol 8, pp. 483 - 499· 0 citations· 42 references
TL;DR
This paper proposes effective multi-agent selective learning methods to boost sample-efficient training by learning from successful experiences, and adopts a retrogression-based selection method to identify successful agent trajectories from the team rewards.
This paper introduces Multi-AGent Preference-Integrated lEarning (MAGPIE), a framework that leverages agent-specific preference signals in the multi-agent learning process and can derive Nash equilibrium solutions.
Ni Mu, Yao Luan, Yiqin Yang et al.· IEEE Transactions on Automat...· 0 citations
This work introduces a novel MARL framework, Multi-Agent Divergence Policy Optimization (MADPO) with Mutual Policy Divergence Maximization (Mutual PDM), and proposes a new extension of CCS divergence for measuring policy divergence of more than two agents, the Generalized Conditional Cauchy-Schwarz (GCCS) divergence.
Haowen Dou, Lujuan Dang, Mingfei Lu et al.· IEEE Transactions on Pattern...· 0 citations
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 has gained significant attention for solving decision-making problems involving multiple autonomous agents. However, effective learning in MARL is still difficult due to environments, dependencies between agents, and poor exploration strategies. Although adaptive exploration and curri...
B. Adwaith, Kevin Francis, Remya Nair T· International Conference on...· 0 citations
Compared with classical reinforcement-learning baselines, in-context learning from trial-and-error experience followed by Experience Distillation matches their performance with at least \(9.6\times\) fewer environment samples.
A hierarchical machine teaching algorithm for reward learning that operates across multiple MDPs and achieves substantially lower regret and stronger generalization to held-out environments than uniform teaching baselines under identical feedback budgets, demonstrating the importance of multi-environment, multi-modal t...
Ali Larian, Qian Lin, Chang-Zong Wu et al.· arXiv.org· 0 citations
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