Henglin Pu — accepted AI-conference papers

2026

Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning

Xuefeng Wang, Lei Zhang, Henglin Pu, Ahmed H Qureshi, Husheng Li

ICLR 2026poster

Existing reinforcement learning (RL) methods struggle with complex dynamical systems that demand interactions at high frequencies or irregular time intervals. Continuous-time RL (CTRL) has emerged as a promising alternative by replacing discrete-time Bellman recursion with differentiable value funct…

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2026

Safe Continuous-time Multi-Agent Reinforcement Learning via Epigraph Form

Xuefeng Wang, Lei Zhang, Henglin Pu, Husheng Li, Ahmed H Qureshi

ICLR 2026poster

Multi-agent reinforcement learning (MARL) has made significant progress in recent years, but most algorithms still rely on a discrete-time Markov Decision Process (MDP) with fixed decision intervals. This formulation is often ill-suited for complex multi-agent dynamics, particularly in high-frequenc…

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