SHORE: Stability-Hybrid On-Policy Reinforcement Learning for Online Bin Packing
Read the original on arXiv Machine Learning →SHORE (Stability‑Hybrid On‑Policy Reinforcement Learning) tackles online 3D bin packing by jointly deciding which box to pack, its orientation, and placement while ensuring stack stability. It learns a stability probability via a 3D U‑Net from both executed outcomes and simulated alternatives, and incorporates these predictions into a discrete on‑policy actor‑critic that gates the influence of an untrained model. In Isaac Gym experiments, SHORE achieves 73 % space utilization in 0.4 M steps—twice as fast as a baseline without stability modeling—and outperforms the GOPT baseline on a 60‑box set, albeit with a slightly higher placement‑failure rate. whyItMatters":"The method demonstrates that integrating learned stability predictions into reinforcement learning accelerates learning and improves packing efficiency in realistic, high‑risk bin‑packing scenarios."
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