Large Scale Constrained Clustering With Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
Given a network, allocating resources at clusters level, rather than at each node, enhances efficiency in resource allocation and usage. In this paper, we study the problem of finding fully connected disjoint clusters to minimize the intra-cluster distances and maximize the number of nodes assigned to the clusters, while also ensuring that no two nodes within a cluster exceed a threshold distance. While the problem can easily be formulated using a binary linear model, traditional combinatorial optimization solvers struggle when dealing with large-scale instances. We propose an approach to solve this constrained clustering problem via reinforcement learning. Our method involves training an agent to generate both feasible and (near) optimal solutions. The agent learns problem-specific heuristics, tailored to the instances encountered in this task. In the results section, we show that our algorithm finds near optimal solutions, even for large scale instances.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 4 references that are in the map. First posted on arXiv 15 Feb 2024.
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References · 4
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
- 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
- 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
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