Shared dynamics learning for large-scale traveling salesman problem
- Publisher:
- ELSEVIER SCI LTD
- Publication Type:
- Journal Article
- Citation:
- Advanced Engineering Informatics, 2023, 56
- Issue Date:
- 2023-04-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S1474034623001337-main.pdf | Published version | 1.37 MB |
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In this work, we study generalization in reinforcement learning for traveling salesman problem (TSP). While efforts have been made for designing deep reinforcement learning-based solvers to achieve near optimal results in small tasks, it is still an open problem to apply such solvers to larger-scale tasks by retaining performance. In this research, we learn the shared dynamics in TSP environments based on multi-task learning, which can be generalized to new tasks. To accurately estimate such dynamics, we consider leveraging the node visitation information. Besides designing RL-based models to attentively aggregate the visitation information during decision making, we propose a scheduled data utilization strategy to stabilize learning with various problem sizes. The experimental result shows that our model achieves improved generalizability for unseen larger TSPs in both zero-shot and few-shot settings.
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