Open access
Aug 2026
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...
Shu-Hao Yang, Min Huang, Shengxiang Yang et al.
· Mathematics · 0 citations