One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem

AI summary

Overview

Research area: Combinatorial optimization and deep reinforcement learning, specifically the three-dimensional bin packing problem (3D-BPP) with physical stability constraints.

Technical level: Advanced. The paper assumes familiarity with reinforcement learning (PPO, actor-critic architectures, entropy and policy-gradient mechanics), attention-based neural networks, and reward shaping.

Scope: A single deep reinforcement learning framework, One4Many-StablePacker (O4M-SP), that is trained once and directly applied to bins of many different dimensions while explicitly enforcing support and weight stability constraints.

What This Paper Is About

3D bin packing asks how to place cuboid items inside a bin so that space utilization is maximized without items overlapping. Existing learning-based packers mostly optimize volume alone and must be retrained whenever bin dimensions change, so they ignore the stability conditions that make a packing physically usable and generalize poorly to new bin sizes. The authors build a reinforcement learning agent that handles variable bin dimensions in one training run and rejects placements that would be unstable because of insufficient base support or excessive vertical load.

Key Contributions

1. O4M-SP framework. The authors present what they describe as the first DRL framework for offline 3D-BPP that simultaneously addresses stability constraints and generalizes across diverse bin dimensions within a single training process ("train once, apply broadly").
2. Weighted reward function. A weighted reward combines a loading-rate term with a new height-difference metric for packing layouts, promoting flatter surfaces and better bin utilization than single-objective rewards.
3. Entropy control at critical decision nodes. Clipped policy gradient optimization with tailored policy drifting is applied at high-covariance nodes and initial-placement nodes to mitigate policy entropy collapse, preserve exploration, and avoid premature convergence to local optima.
4. Empirical validation of stability and generalization. Experiments show superior packing performance over baseline methods

Authors’ abstract

The three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing. Existing learning-based approaches often neglect practical stability-related constraints and exhibit limitations in generalizing across diverse bin dimensions. To address these limitations, we propose a novel deep reinforcement learning framework, One4Many-StablePacker (O4M-SP). The primary advantage of O4M-SP is its ability to handle various bin dimensions in a single training process while incorporating support and weight constraints common in practice. Our training method introduces two innovative mechanisms. First, it employs a weighted reward function that integrates loading rate and a new height difference metric for packing layouts, promoting improved bin utilization through flatter packing configurations. Second, it combines clipped policy gradient optimization with a tailored policy drifting method to mitigate policy entropy collapse, encouraging exploration at critical decision nodes during packing to avoid suboptimal solutions. Extensive experiments demonstrate that O4M-SP generalizes successfully across diverse bin dimensions and significantly outperforms baseline methods. Furthermore, O4M-SP exhibits strong practical applicability by effectively addressing packing scenarios with stability constraints.