  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.
