Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
Autor: | Liang Xin, Jie Zhang, Jingwen Li, Andrew Lim, Wen Song, Zhiguang Cao |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Artificial neural network Heuristic (computer science) Computer science business.industry Mechanical Engineering Distributed computing Deep learning Node (networking) Computer Science Applications Machine Learning (cs.LG) Constraint (information theory) Automotive Engineering Vehicle routing problem Leverage (statistics) Reinforcement learning Artificial intelligence business |
Popis: | Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i.e., the pickup node must precede the pairing delivery node. Further integrated with a masking scheme, the learnt policy is expected to find higher-quality solutions for solving PDP. Extensive experimental results show that our method outperforms the state-of-the-art heuristic and deep learning model, respectively, and generalizes well to different distributions and problem sizes. This paper has been accepted at IEEE Transactions on Intelligent Transportation Systems |
Databáze: | OpenAIRE |
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