Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization

arXiv cs.RO / 4/14/2026

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Key Points

The paper addresses the online 3D bin packing problem for logistics and manufacturing, noting that prior deep reinforcement learning approaches often suffer from low sample efficiency.
It introduces a diffusion reinforcement learning framework that models packing as a Markov decision chain and uses a height-map-based state representation.
The actor network is driven by a diffusion model, aiming to improve decision quality in complex online packing scenarios.
Experimental results report a significant improvement in the average number of packed items versus state-of-the-art DRL methods, suggesting strong practical applicability.

Abstract

The online 3D bin packing problem is important in logistics, warehousing and intelligent manufacturing, with solutions shifting to deep reinforcement learning (DRL) which faces challenges like low sample efficiency. This paper proposes a diffusion reinforcement learning-based algorithm, using a Markov decision chain for packing modeling, height map-based state representation and a diffusion model-based actor network. Experiments show it significantly improves the average number of packed items compared to state-of-the-art DRL methods, with excellent application potential in complex online scenarios.