arXiv:2606. 14130v1 Announce Type: new Abstract: Safe coordination problems surface in multi-agent reinforcement learning when global safety cannot be enforced by any agent unilaterally: the admissibility of one agent's action may depend on the dynamics of other agents.
By Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli
arXiv:2606. 30072v1 Announce Type: new Abstract: Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return.
By Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner, Pascal Poupart, Jongmin Lee, Kee-Eung Kim
arXiv:2608. 06735v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved strong results in improving large language models (LLMs) on tasks with stationary, verifiable rewards, such as mathematical reasoning and code execution.
By Senhao Wang, Chenghao Cai, Haitao Hu, Mingxing Huang, Xingguang Wang, Wenhao Li, Zecheng Lin
arXiv:2609.08452v2 Announce Type: replace
Abstract: Multi-agent systems enable complex reasoning and tool use by coordinating agents that divide roles and refine candidate solutions. Existing methods...
By Shengtian Yang, Ziyu Xiong, Yu Li, Yewen Li, Qingpeng Cai, Lei Feng
arXiv:2606. 25526v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region framework of single-agent.
By Bang Giang Le, Viet Cuong Ta
RideSkill is a hierarchical algorithm for generalized ride sharing that uses large language models (LLMs) to automatically design and train a skill repository, a combiner, and a repositioner. The combiner assigns vehicle-specific skills for adaptive dispatch across varying scenarios and objectives, while the repositioner moves idle vehicles to emerging regions to avoid conflicts. By training all components via an LLM-based evolutionary method, RideSkill eliminates the need for real-time LLM calls, enabling high-performance deployment in large-scale systems.
By Zijian Zhao, Sen Li, Xialiang Tong, Mingxuan Yuan