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[2602.17276] RLGT: A reinforcement learning framework for extremal graph theory
Summary
The paper introduces RLGT, a novel reinforcement learning framework designed for extremal graph theory, enhancing the application of RL in solving combinatorial optimization problems.
Why It Matters
This research is significant as it builds upon previous work in applying reinforcement learning to graph theory, providing a structured approach that could lead to new discoveries and optimizations in extremal graph theory. It aims to streamline future research efforts and improve computational efficiency.
Key Takeaways
- RLGT systematizes previous reinforcement learning frameworks for graph theory.
- The framework supports various graph types, enhancing versatility.
- It addresses significant problems in extremal graph theory, including Ramsey numbers and spectral radius inequalities.
- Optimized computational performance is a key feature of RLGT.
- The modular design of RLGT facilitates future research and development.
Computer Science > Machine Learning arXiv:2602.17276 (cs) [Submitted on 19 Feb 2026] Title:RLGT: A reinforcement learning framework for extremal graph theory Authors:Ivan Damnjanović, Uroš Milivojević, Irena Đorđević, Dragan Stevanović View a PDF of the paper titled RLGT: A reinforcement learning framework for extremal graph theory, by Ivan Damnjanovi\'c and 3 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) is a subfield of machine learning that focuses on developing models that can autonomously learn optimal decision-making strategies over time. In a recent pioneering paper, Wagner demonstrated how the Deep Cross-Entropy RL method can be applied to tackle various problems from extremal graph theory by reformulating them as combinatorial optimization problems. Subsequently, many researchers became interested in refining and extending the framework introduced by Wagner, thereby creating various RL environments specialized for graph theory. Moreover, a number of problems from extremal graph theory were solved through the use of RL. In particular, several inequalities concerning the Laplacian spectral radius of graphs were refuted, new lower bounds were obtained for certain Ramsey numbers, and contributions were made to the Turán-type extremal problem in which the forbidden structures are cycles of length three and four. Here, we present Reinforcement Learning for Graph Theory (RLGT), a novel RL framework that systematizes the previous work and...