2026-06-18原文2 min readUpdated: 2026-06-18

N(CO)$^2$: Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering

This paper introduces N(CO)$^2$, a reinforcement learning-based neural combinatorial optimization framework that solves the Stochastic Orienteering Problem without hand-crafted heuristics. It achieves competitive performance with state-of-the-art mixed-integer linear programming across diverse instances, reducing human design effort.

SourcearXiv RoboticsAuthor: Anas Saeed, Marcos Abel Zuzu\'arregui, Stefano Carpin

[2606.18514] N(CO)$^2$: Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering

[Submitted on 16 Jun 2026]

Title:N(CO)$^2$: Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering

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Abstract:Neural combinatorial optimization (NCO) offers a promising alternative to traditional heuristic-based methods for solving complex graph optimization problems by proposing to learn heuristics through data. This class of problems frequently arises in automation, as it can be used to model a variety of applications. While NCO has been extensively studied for deterministic combinatorial optimization problems, there are only a few works that aim to solve stochastic combinatorial optimization problems. In this work, we present N(CO)$^2$: Neural Combinatorial Optimization with Chance cOnstraints to solve the Stochastic Orienteering Problem (SOP) without the use of hand-crafted heuristics. By integrating a reinforcement learning (RL) framework, the model optimizes path selection under uncertainty, effectively balancing exploration and exploitation. Empirical results demonstrate that our method generalizes well across diverse SOP instances, achieving competitive performance compared to the state-of-the-art mixed-integer linear program (MILP) for the task. The proposed approach reduces human effort in heuristic design while enabling adaptive and efficient decision-making in uncertain environments.

Subjects:

Robotics (cs.RO); Machine Learning (cs.LG)

Cite as: arXiv:2606.18514 [cs.RO]

(or arXiv:2606.18514v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.18514

arXiv-issued DOI via DataCite (pending registration)

Journal reference: In Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), 2025

Submission history

From: Marcos Abel Zuzuárregui [view email] [v1] Tue, 16 Jun 2026 22:05:51 UTC (3,955 KB)

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