Related papers: Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

URL: http://arxiv.org/abs/2110.02634v1
Date: Wed, 6 Oct 2021 10:16:07 GMT
Title: Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
Authors: Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
Abstract summary: 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 prescribes attentions for each role of the nodes while taking into account the precedence constraint. Our method outperforms the state-of-the-art and deep learning model, respectively, and generalizes well to different distributions and problem sizes.
Score: 14.627657852087994
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.