Shu-Hao Yang

End-to-End Graph-Embedded Reinforcement Learning for Solving the Shortest Path Problem with Constraints

This study introduces E2E_GERL, a novel end-to-end graph-embedded reinforcement learning algorithm for the time-constrained SPP, which achieves better results with substantially lower inference time than classical and NCO baselines, which also validate the potential of integrating NCO into constrained optimization prob...