Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.16721 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910041215336448 |
|---|---|
| author | Shin, Min Kyu Park, Su-Jeong Ryu, Seung-Keol Kim, Heeyeon Choi, Han-Lim |
| author_facet | Shin, Min Kyu Park, Su-Jeong Ryu, Seung-Keol Kim, Heeyeon Choi, Han-Lim |
| contents | This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement learning approach leverages privileged information to distill knowledge from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm. Subsequently, a supervised learning phase trains an adaptation network to solve problems independently of privileged information. Before the first learning phase, a parameter initialization technique using the demonstration data was also devised to enhance training efficiency. The proposed learning method produces a solution about 50 times faster than LKH and substantially outperforms other imitation learning and RL with demonstration schemes, most of which fail to sense all the task points. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods Shin, Min Kyu Park, Su-Jeong Ryu, Seung-Keol Kim, Heeyeon Choi, Han-Lim Artificial Intelligence Machine Learning This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement learning approach leverages privileged information to distill knowledge from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm. Subsequently, a supervised learning phase trains an adaptation network to solve problems independently of privileged information. Before the first learning phase, a parameter initialization technique using the demonstration data was also devised to enhance training efficiency. The proposed learning method produces a solution about 50 times faster than LKH and substantially outperforms other imitation learning and RL with demonstration schemes, most of which fail to sense all the task points. |
| title | Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.16721 |