Many learning tasks require observing a sequence of images and making a decision. In a transportation problem of designing and planning for shipping boxes between nodes, we show how to treat the network of nodes and the flows between them as images. These images have useful structural information that can be statistically summarized. Using image compression techniques, we reduce an image down to a set of numbers that contain interpretable geographic information that we call geographic signatures. Using geographic signatures, we learn network structure that can be utilized to recommend future network connectivity. We develop a Bayesian reinforcement algorithm that takes advantage of statistically summarized network information as priors and user-decisions to reinforce an agent's probabilistic decision. Additionally, we show how reinforcement learning can be used with compression directly without interpretation in simple tasks.

翻译：许多学习任务需要观察图像序列并做出决策。在涉及节点间运输箱体设计与规划的运输问题中，我们展示了如何将节点网络及其流量视为图像。这些图像包含可通过统计汇总的有用结构信息。利用图像压缩技术，我们将图像简化为包含可解释地理信息的一组数值，并将其称为地理签名。通过地理签名，我们学习可用于推荐未来网络连接的网络结构。我们开发了一种贝叶斯强化算法，该算法利用统计汇总的网络信息作为先验知识，并结合用户决策来强化智能体的概率决策。此外，我们展示了在简单任务中无需解释即可直接结合压缩技术与强化学习的方法。