2025 Poster "multi-agent reinforcement learning" Papers
19 papers found
Advantage Alignment Algorithms
Juan Duque, Milad Aghajohari, Timotheus Cooijmans et al.
A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning
Anjie Liu, Jianhong Wang, Samuel Kaski et al.
DoF: A Diffusion Factorization Framework for Offline Multi-Agent Reinforcement Learning
Chao Li, Ziwei Deng, Chenxing Lin et al.
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels
Alexander DeRieux, Walid Saad
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning
Xinran Li, Xiaolu Wang, Chenjia Bai et al.
FlickerFusion: Intra-trajectory Domain Generalizing Multi-agent Reinforcement Learning
Woosung Koh, Wonbeen Oh, Siyeol Kim et al.
High-order Interactions Modeling for Interpretable Multi-Agent Q-Learning
Qinyu Xu, Yuanyang Zhu, Xuefei Wu et al.
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
Yiqun Chen, Lingyong Yan, Weiwei Sun et al.
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher Collins, George Darling et al.
MALinZero: Efficient Low-Dimensional Search for Mastering Complex Multi-Agent Planning
Sizhe Tang, Jiayu Chen, Tian Lan
MOSDT: Self-Distillation-Based Decision Transformer for Multi-Agent Offline Safe Reinforcement Learning
Yuchen Xia, Yunjian Xu
Multi-Agent Reinforcement Learning with Communication-Constrained Priors
Guang Yang, Jingwen Qiao, Tianpei Yang et al.
OPHR: Mastering Volatility Trading with Multi-Agent Deep Reinforcement Learning
Zeting Chen, Xinyu Cai, Molei Qin et al.
Rainbow Delay Compensation: A Multi-Agent Reinforcement Learning Framework for Mitigating Observation Delays
Songchen Fu, Siang Chen, Shaojing Zhao et al.
ReMA: Learning to Meta-Think for LLMs with Multi-agent Reinforcement Learning
Ziyu Wan, Yunxiang Li, Xiaoyu Wen et al.
Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization
Tianxu Li, Kun Zhu
Trajectory-Class-Aware Multi-Agent Reinforcement Learning
Hyungho Na, Kwanghyeon Lee, Sumin Lee et al.
Wonder Wins Ways: Curiosity-Driven Exploration through Multi-Agent Contextual Calibration
Yiyuan Pan, Zhe Liu, Hesheng Wang