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The 3D Bin Packing Problem (3D-BPP) is one of the most demanded yetchallenging problems in industry, where an agent must pack variable size itemsdelivered in sequence into a finite bin with the aim to maximize the spaceutilization. It represents a strongly NP-Hard optimization problem such that nosolution has been offered to date with high performance in space utilization.In this paper, we present a new reinforcement learning (RL) framework for a3D-BPP solution for improving performance. First, a buffer is introduced toallow multi-item action selection. By increasing the degree of freedom inaction selection, a more complex policy that results in better packingperformance can be derived. Second, we propose an agnostic data augmentationstrategy that exploits both bin item symmetries for improving sampleefficiency. Third, we implement a model-based RL method adapted from thepopular algorithm AlphaGo, which has shown superhuman performance in zero-sumgames. Our adaptation is capable of working in single-player and score basedenvironments. In spite of the fact that AlphaGo versions are known to becomputationally heavy, we manage to train the proposed framework with a singlethread and GPU, while obtaining a solution that outperforms thestate-of-the-art results in space utilization.

Online 3D Bin Packing Reinforcement Learning Solution with Buffer