5 papers
Cooperating with Future Collaborators: Multi-Agent RL under Staggered Participation
Jianglin Qiao, Siyi Hu, Thien Hoang Nguyen +2
In cooperative Multi-Agent Reinforcement Learning (MARL), agents are often trained under concurrent participation, while in many tasks some agents act earlier and leave task-releva…
Can We Trust the Teacher? Decoupled Credit Direction-Magnitude for Self-Distillation
Yugu Li, Zehong Cao, Peizhen Li +3
RLVR provides reliable trajectory-level credit, while OPSD offers dense supervision for token-level credit. This exposes a fundamental coupling when updating step-level credit dire…
RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training
Yugu Li, Zehong Cao, Jianglin Qiao +1
Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, and conduct iterative search…
Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review
Siyi Hu, Mohamad A Hady, Jianglin Qiao +3
Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the assumptions under which algorithms are des…
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
Yugu Li, Zehong Cao, Jianglin Qiao +1
In cooperative multi-agent reinforcement learning (MARL), agents typically form a single grand coalition based on credit assignment to tackle a composite task, often resulting in s…