  Shared autonomy refers to approaches for enabling an autonomous agent to
collaborate with a human with the aim of improving human performance. However,
besides improving performance, it may often also be beneficial that the agent
concurrently accounts for preserving the user's experience or satisfaction of
collaboration. In order to address this additional goal, we examine approaches
for improving the user experience by constraining the number of interventions
by the autonomous agent. We propose two model-free reinforcement learning
methods that can account for both hard and soft constraints on the number of
interventions. We show that not only does our method outperform the existing
baseline, but also eliminates the need to manually tune a black-box
hyperparameter for controlling the level of assistance. We also provide an
in-depth analysis of intervention scenarios in order to further illuminate
system understanding.
