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cs.MA2025
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Xinran Li, Chenjia Bai, Zijian Li +3
Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environm…
cs.MA2025
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
Yang Zhang, Xinran Li, Jianing Ye +5
World models have recently attracted growing interest in Multi-Agent Reinforcement Learning (MARL) due to their ability to improve sample efficiency for policy learning. However, a…
cs.MA2025
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning
Xinran Li, Xiaolu Wang, Chenjia Bai +1
In cooperative multi-agent reinforcement learning (MARL), well-designed communication protocols can effectively facilitate consensus among agents, thereby enhancing task performanc…