Improving Generalization of Deep Reinforcement Learning-based TSP Solvers

• URL: http://arxiv.org/abs/2110.02843v1
• Date: Wed, 6 Oct 2021 15:16:19 GMT
• Title: Improving Generalization of Deep Reinforcement Learning-based TSP Solvers
• Authors: Wenbin Ouyang, Yisen Wang, Shaochen Han, Zhejian Jin and Paul Weng
• Abstract summary: We propose a novel approach named MAGIC that includes a deep learning architecture and a DRL training method. Our architecture, which integrates a multilayer perceptron, a graph neural network, and an attention model, defines a policy that sequentially generates a traveling salesman solution. Our training method includes several innovations: (1) we interleave DRL policy updates with local search (using a new local search technique), (2) we use a novel simple baseline, and (3) we apply gradient learning.
• Score: 19.29028564568974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work applying deep reinforcement learning (DRL) to solve traveling salesman problems (TSP) has shown that DRL-based solvers can be fast and competitive with TSP heuristics for small instances, but do not generalize well to larger instances. In this work, we propose a novel approach named MAGIC that includes a deep learning architecture and a DRL training method. Our architecture, which integrates a multilayer perceptron, a graph neural network, and an attention model, defines a stochastic policy that sequentially generates a TSP solution. Our training method includes several innovations: (1) we interleave DRL policy gradient updates with local search (using a new local search technique), (2) we use a novel simple baseline, and (3) we apply curriculum learning. Finally, we empirically demonstrate that MAGIC is superior to other DRL-based methods on random TSP instances, both in terms of performance and generalizability. Moreover, our method compares favorably against TSP heuristics and other state-of-the-art approach in terms of performance and computational time.

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