  The 3D Bin Packing Problem (3D-BPP) is one of the most demanded yet
challenging problems in industry, where an agent must pack variable size items
delivered in sequence into a finite bin with the aim to maximize the space
utilization. It represents a strongly NP-Hard optimization problem such that no
solution has been offered to date with high performance in space utilization.
In this paper, we present a new reinforcement learning (RL) framework for a
3D-BPP solution for improving performance. First, a buffer is introduced to
allow multi-item action selection. By increasing the degree of freedom in
action selection, a more complex policy that results in better packing
performance can be derived. Second, we propose an agnostic data augmentation
strategy that exploits both bin item symmetries for improving sample
efficiency. Third, we implement a model-based RL method adapted from the
popular algorithm AlphaGo, which has shown superhuman performance in zero-sum
games. Our adaptation is capable of working in single-player and score based
environments. In spite of the fact that AlphaGo versions are known to be
computationally heavy, we manage to train the proposed framework with a single
thread and GPU, while obtaining a solution that outperforms the
state-of-the-art results in space utilization.
