A knowledge-embedded RL framework decomposes generalized CVRPs into route-first and cluster-second subproblems, using dynamic programming to guide the RL solver and a history-enhanced context module to handle partial observability, yielding better solutions than prior learning methods.
Sym-nco: Leveraging symmetricity for neural com- binatorial optimization.Advances in Neural Information Processing Systems, 35:1936–1949,
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A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems
A knowledge-embedded RL framework decomposes generalized CVRPs into route-first and cluster-second subproblems, using dynamic programming to guide the RL solver and a history-enhanced context module to handle partial observability, yielding better solutions than prior learning methods.