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cs.LG2025
Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning
Vittorio Giammarino, Ahmed H. Qureshi
Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In th…
cs.LG2025
Physics-informed Value Learner for Offline Goal-Conditioned Reinforcement Learning
Vittorio Giammarino, Ruiqi Ni, Ahmed H. Qureshi
Offline Goal-Conditioned Reinforcement Learning (GCRL) holds great promise for domains such as autonomous navigation and locomotion, where collecting interactive data is costly and…
cs.LG2025
Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
Xuefeng Wang, Lei Zhang, Henglin Pu +2
Existing reinforcement learning (RL) methods struggle with complex dynamical systems that demand interactions at high frequencies or irregular time intervals. Continuous-time RL (C…