We introduce the transport-and-pack (TAP) problem, a frequently encountered instance of real-world packing, and develop a neural optimization solution based on reinforcement learning. Given an initial spatial configuration of boxes, we seek an efficient method to iteratively transport and pack the boxes compactly into a target container. Due to obstruction and accessibility constraints, our problem has to add a new search dimension, i.e., finding an optimal transport sequence , to the already immense search space for packing alone. Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search. In our work, we represent the transport constraints using a precedence graph and train a neural network, coined TAP-Net, using reinforcement learning to reward efficient and stable packing. The network is built on an encoder-decoder architecture, where the encoder employs convolution layers to encode the box geometry and precedence graph and the decoder is a recurrent neural network (RNN) which inputs the current encoder output, as well as the current box packing state of the target container, and outputs the next box to pack, as well as its orientation. We train our network on randomly generated initial box configurations, without supervision , via policy gradients to learn optimal TAP policies to maximize packing efficiency and stability. We demonstrate the performance of TAP-Net on a variety of examples, evaluating the network through ablation studies and comparisons to baselines and alternative network designs. We also show that our network generalizes well to larger problem instances, when trained on small-sized inputs.

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我们介绍运输和包装（TAP）问题，一个在现实世界中经常遇到的打包实例，并开发一个神经优化解决方案基于强化学习。给定盒子的初始空间配置，我们寻求一种有效的方法来迭代运输并将盒子紧凑地打包到目标容器中。由于障碍和可达性的限制，我们的问题必须增加一个新的搜索维度，即找到一个最优的运输顺序，到已经巨大的搜索空间单独打包。使用基于学习的方法，经过训练的网络可以学习和编码解决方案模式以指导新问题实例的解决方案，而不是执行昂贵的在线搜索。在我们的工作中，我们使用优先图并训练一个神经网络，创造了 TAP-Net，使用强化学习来奖励高效的和稳定的包装。该网络建立在编码器-解码器架构上，其中编码器使用卷积层对框几何和优先图进行编码，解码器是循环神经网络 (RNN)，它输入当前编码器输出以及当前框打包目标容器的状态，并输出下一个要打包的盒子，以及它的方向。我们训练我们的网络随机生成初始盒子配置，没有监督，通过策略梯度来学习最优的 TAP 策略，以最大限度地提高打包效率和稳定性。我们在各种示例中展示了 TAP-Net 的性能，通过消融研究以及与基线和替代网络设计的比较来评估网络。我们还表明，当在小型输入上进行训练时，我们的网络可以很好地推广到更大的问题实例。