  Modern virtual personal assistants provide a convenient interface for
completing daily tasks via voice commands. An important consideration for these
assistants is the ability to recover from automatic speech recognition (ASR)
and natural language understanding (NLU) errors. In this paper, we focus on
learning robust dialog policies to recover from these errors. To this end, we
develop a user simulator which interacts with the assistant through voice
commands in realistic scenarios with noisy audio, and use it to learn dialog
policies through deep reinforcement learning. We show that dialogs generated by
our simulator are indistinguishable from human generated dialogs, as determined
by human evaluators. Furthermore, preliminary experimental results show that
the learned policies in noisy environments achieve the same execution success
rate with fewer dialog turns compared to fixed rule-based policies.
