Imitation-regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning

arXiv:2402.17967v1 Announce Type: new
Abstract: Network systems form the foundation of modern society, playing a critical role in various applications. However, these systems are at significant risk of being adversely affected by unforeseen circumstances, such as disasters. Considering this, there is a pressing need for research to enhance the robustness of network systems. Recently, in reinforcement learning, the relationship between acquiring robustness and regularizing entropy has been identified. Additionally, imitation learning is used within this framework to reflect experts' behavior. …

abstract application applications arxiv cs.lg cs.sy eess.sy form foundation logistics modern network networks planning playing research risk robustness role society systems transport type