We present AutoDOViz, an interactive user interface for automated decision
optimization (AutoDO) using reinforcement learning (RL). Decision optimization
(DO) has classically being practiced by dedicated DO researchers where experts
need to spend long periods of time fine tuning a solution through
trial-and-error. AutoML pipeline search has sought to make it easier for a data
scientist to find the best machine learning pipeline by leveraging automation
to search and tune the solution. More recently, these advances have been
applied to the domain of AutoDO, with a similar goal to find the best
reinforcement learning pipeline through algorithm selection and parameter
tuning. However, Decision Optimization requires significantly more complex
problem specification when compared to an ML problem. AutoDOViz seeks to lower
the barrier of entry for data scientists in problem specification for
reinforcement learning problems, leverage the benefits of AutoDO algorithms for
RL pipeline search and finally, create visualizations and policy insights in
order to facilitate the typical interactive nature when communicating problem
formulation and solution proposals between DO experts and domain experts. In
this paper, we report our findings from semi-structured expert interviews with
DO practitioners as well as business consultants, leading to design
requirements for human-centered automation for DO with RL. We evaluate a system
implementation with data scientists and find that they are significantly more
open to engage in DO after using our proposed solution. AutoDOViz further
increases trust in RL agent models and makes the automated training and
evaluation process more comprehensible. As shown for other automation in ML
tasks, we also conclude automation of RL for DO can benefit from user and
vice-versa when the interface promotes human-in-the-loop.

本文介绍一种基于自动决策优化和强化学习的用户交互界面 AutoDOViz，旨在降低数据科学家在问题规范上的门槛，利用 AutoDO 算法搜索 RL 管道并创建可视化和方针洞悉，为 DO 专家和领域专家之间的沟通提供便利。经过 DO 实践者和业务顾问的半结构化专家访谈，我们提出了人性化自动化 DO 的设计需求，并通过数据科学家的系统实现评估发现，使用我们提出的解决方案后，他们显著更愿意参与 DO，同时 AutoDOViz 进一步增加对 RL 代理模型的信任，使自动训练和评估过程更易理解。我们还得出结论，当界面促进人的参与时，自动化 RL 可以从用户中受益，反之亦然。