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OtoWorld Towards Learning to Separate by Learning to Move

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Document pages: 7 pages

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

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