Cavity-Aware Deep Reinforcement Learning for 3D Bin Packing

The three-dimensional bin packing problem (3D-BPP) is a classical optimization problem in logistics and transportation. Existing methods often rely on handcrafted heuristics or simplified state representations, which limits their ability to capture complex spatial relationships in packing environments. To ad-dress this issue, this paper proposes Cavity-Map-based Deep Reinforcement Learning (CMDRL) for the 3D-BPP. The proposed method introduces a cavity-map representation to model the geometric structure of free space and designs an enhanced spatiotemporal attention mechanism to jointly capture packing se-quence dependencies and spatial layout information. A Transformer-based policy network trained with the Soft Actor-Critic (SAC) algorithm is developed to gen-erate efficient packing decisions. Experimental results show that the proposed method consistently outperforms several state-of-the-art DRL baselines in terms of gap ratio. Ablation studies further demonstrate the effectiveness of the cavity map and the spatiotemporal attention mechanism.