3 papers
cs.MA2025
Heterogeneity in Multi-Agent Reinforcement Learning
Tianyi Hu, Zhiqiang Pu, Yuan Wang +3
Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy…
cs.MA2025
MA2RL: Masked Autoencoders for Generalizable Multi-Agent Reinforcement Learning
Jinyuan Feng, Min Chen, Zhiqiang Pu +2
To develop generalizable models in multi-agent reinforcement learning, recent approaches have been devoted to discovering task-independent skills for each agent, which generalize a…
cs.AI2025
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
Hao Ma, Tianyi Hu, Zhiqiang Pu +4
Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominan…