  We present OtoWorld, an interactive environment in which agents must learn to
listen in order to solve navigational tasks. The purpose of OtoWorld is to
facilitate reinforcement learning research in computer audition, where agents
must learn to listen to the world around them to navigate. OtoWorld is built on
three open source libraries: OpenAI Gym for environment and agent interaction,
PyRoomAcoustics for ray-tracing and acoustics simulation, and nussl for
training deep computer audition models. OtoWorld is the audio analogue of
GridWorld, a simple navigation game. OtoWorld can be easily extended to more
complex environments and games. To solve one episode of OtoWorld, an agent must
move towards each sounding source in the auditory scene and "turn it off". The
agent receives no other input than the current sound of the room. The sources
are placed randomly within the room and can vary in number. The agent receives
a reward for turning off a source. We present preliminary results on the
ability of agents to win at OtoWorld. OtoWorld is open-source and available.

Sound;Machine Learning;Audio and Speech Processing;Machine Learning