Normalized Cut with Reinforcement Learning in Constrained Action Space

• URL: http://arxiv.org/abs/2505.13986v2
• Date: Fri, 23 May 2025 06:35:56 GMT
• Title: Normalized Cut with Reinforcement Learning in Constrained Action Space
• Authors: Qize Jiang, Linsey Pang, Alice Gatti, Mahima Aggarwal, Giovanna Vantini, Xiaosong Ma, Weiwei Sun, Sanjay Chawla,
• Abstract summary: We propose the first RL solution that uses constrained action spaces to guide the normalized cut problem towards pre-defined template instances.<n>Using transportation networks as an example domain, we create a Wedge and Ring Transformer that results in graph partitions that are shaped in form of Wedges and Rings.
• Score: 13.817172228740523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) has emerged as an important paradigm to solve combinatorial optimization problems primarily due to its ability to learn heuristics that can generalize across problem instances. However, integrating external knowledge that will steer combinatorial optimization problem solutions towards domain appropriate outcomes remains an extremely challenging task. In this paper, we propose the first RL solution that uses constrained action spaces to guide the normalized cut problem towards pre-defined template instances. Using transportation networks as an example domain, we create a Wedge and Ring Transformer that results in graph partitions that are shaped in form of Wedges and Rings and which are likely to be closer to natural optimal partitions. However, our approach is general as it is based on principles that can be generalized to other domains.

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