🤖 AI Summary
This work addresses the limitations of traditional two-dimensional irregular nesting methods, which often suffer from insufficient geometric awareness and reliance on inefficient brute-force search strategies. To overcome these challenges, the authors propose a data-driven approach that integrates a geometry-aware neural encoder with reinforcement learning. The core innovation lies in the design of a Polygon Transformer (PoT) architecture featuring a cross-polygon attention mechanism, alongside the introduction of the first open-source training and evaluation benchmark tailored for complex geometric contours. Trained within a Combinatorial Optimization via Reinforcement Learning (CORL) framework, the resulting agent achieves area utilization performance on par with Sparrow, the current state-of-the-art heuristic solver, while significantly improving exploration efficiency in continuous placement spaces.
📝 Abstract
Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance. In this paper, we argue that Reinforcement Learning is uniquely positioned to overcome this bottleneck. By pairing an optimization policy with a geometry-aware neural encoder, an agent can automatically discover rich geometric priors directly from data, utilizing these learned intuitions to strategically guide exploration. To realize this, we introduce the Polygons Transformer (PoT), a novel architecture that encodes 2D continuous vector geometries while allowing cross-polygons attention. We couple this novel architecture with a Combinatorial Optimization Reinforcement Learning (CORL) training framework to find optimal solutions. To support this paradigm, we release an open-source training dataset derived from complex geographic contours alongside a dedicated evaluation benchmark. Our empirical validation demonstrates that our trained agent achieves area utilization performance highly competitive with Sparrow, the state-of-the-art heuristic solver, proving that reinforcement learning can successfully discover and exploit geometric awareness for precise spatial tasks.