  In this work we use deep reinforcement learning to create an autonomous agent
that can navigate in a two-dimensional space using only raw auditory sensory
information from the environment, a problem that has received very little
attention in the reinforcement learning literature. Our experiments show that
the agent can successfully identify a particular target speaker among a set of
$N$ predefined speakers in a room and move itself towards that speaker, while
avoiding collision with other speakers or going outside the room boundaries.
The agent is shown to be robust to speaker pitch shifting and it can learn to
navigate the environment, even when a limited number of training utterances are
available for each speaker.
