GAMMA: Graph Attention Model for Multiple Agents to Solve Team Orienteering Problem With Multiple Depots

In this work, we present an attention-based encoder-decoder model to approximately solve the team orienteering problem with multiple depots (TOPMD). The TOPMD instance is an NP-hard combinatorial optimization problem that involves multiple agents (or autonomous vehicles) and not purely Euclidean (st...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 11; s. 9412 - 9423
Hlavní autori:	Sankaran, Prashant, McConky, Katie, Sudit, Moises, Ortiz-Pena, Hector
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autonomous vehicles Combinatorial analysis Combinatorial optimization (CO) methods Computational modeling Computer architecture Configurations Decoding graph attention models Heuristic methods Integer programming Machine learning machine learning (ML) algorithms Mathematical models Metaheuristics Mission planning Mixed integer Multiagent systems multiple autonomous vehicles Optimization Orienteering Problem solving Reconnaissance reinforcement learning Stochasticity Straight lines Synthetic data Training
ISSN:	2162-237X, 2162-2388, 2162-2388
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Popis
Shrnutí:	In this work, we present an attention-based encoder-decoder model to approximately solve the team orienteering problem with multiple depots (TOPMD). The TOPMD instance is an NP-hard combinatorial optimization problem that involves multiple agents (or autonomous vehicles) and not purely Euclidean (straight line distance) graph edge weights. In addition, to avoid tedious computations on dataset creation, we provide an approach to generate synthetic data on the fly for effectively training the model. Furthermore, to evaluate our proposed model, we conduct two experimental studies on the multi-agent reconnaissance mission planning problem formulated as TOPMD. First, we characterize the model based on the training configurations to understand the scalability of the proposed approach to unseen configurations. Second, we evaluate the solution quality of the model against several baselines-heuristics, competing machine learning (ML), and exact approaches, on several reconnaissance scenarios. The experimental results indicate that training the model with a maximum number of agents, a moderate number of targets (or nodes to visit), and moderate travel length, performs well across a variety of conditions. Furthermore, the results also reveal that the proposed approach offers a more tractable and higher quality (or competitive) solution in comparison with existing attention-based models, stochastic heuristic approach, and standard mixed-integer programming solver under the given experimental conditions. Finally, the different experimental evaluations reveal that the proposed data generation approach for training the model is highly effective.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2162-237X 2162-2388 2162-2388
DOI:	10.1109/TNNLS.2022.3159671