  Staged rollout is a strategy of incrementally releasing software updates to
portions of the user population in order to accelerate defect discovery without
incurring catastrophic outcomes such as system wide outages. Some past studies
have examined how to quantify and automate staged rollout, but stop short of
simultaneously considering multiple product or process metrics explicitly. This
paper demonstrates the potential to automate staged rollout with
multi-objective reinforcement learning in order to dynamically balance
stakeholder needs such as time to deliver new features and downtime incurred by
failures due to latent defects.
