Applications and systems are constantly faced with decisions to make, often using a policy to pick from a set of actions based on some contextual information. We create a service that uses machine learning to accomplish this goal. The service uses exploration, logging, and online learning to create a counterfactually sound system supporting a full data lifecycle. The system is general: it works for any discrete choices, with respect to any reward metric, and can work with many learning algorithms and feature representations. The service has a simple API, and was designed to be modular and reproducible to ease deployment and debugging, respectively. We demonstrate how these properties enable learning systems that are robust and safe. Our evaluation shows that the Decision Service makes decisions in real time and incorporates new data quickly into learned policies. A large-scale deployment for a personalized news website has been handling all traffic since Jan. 2016, resulting in a 25% relative lift in clicks. By making the Decision Service externally available, we hope to make optimal decision making available to all.

本论文提出 Decision Service，一个支持诸如强化学习等决策任务的通用系统，利用探索、日志、学习和部署这四个系统抽象不断学习并做出实时决策，可广泛应用于内容推荐、技术支持和机器故障处理等各领域，从而大幅降低实现相关算法的技术负债。

低技术债务下的上下文决策