Solving 3D packing problem using Transformer network and reinforcement learning

The three-dimensional packing problem (3D-PP) is a classic NP-hard problem in operations research and computer science. One of the most popular ways to solve the problem is heuristic methods with a search strategy. However, approaches based on machine learning have recently received widespread attention because of their efficiency. In this work, we propose a deep reinforcement learning (DRL) model to solve 3D-PP. Our method employs Transformer architecture as the policy network and uses Proximal Policy Optimization (PPO) to train the network. Compared with previous approaches using DRL, our method presents a novel state representation of packing environment, and introduces plane features for representing the length and width information of container. Our method achieves the new state-of-the-art results for using DRL to solve 3D-PP. The code of our method will be released to facilitate future research.