2025-01-12-debner25a.md

title	openreview	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Towards concurrent real-time audio-aware agents with deep reinforcement learning	UQuETmoMQX	https://github.com/Aalto-ESG/aaaa-2025	Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents. We use spatial audio libraries in combination with deep reinforcement learning to allow agents to observe their surroundings and to navigate in their environment using audio cues. In general, game engines support rendering audio for one agent only. Using a hide-and-seek scenario in our experimentation we show how support for multiple concurrent listeners can be used to parallelize the runtime operation and to enable using multiple agents. Further, we analyze the effects of audio environment complexity to demonstrate the scalability of our approach.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	debner25a	0	Towards concurrent real-time audio-aware agents with deep reinforcement learning	32	40	32-40	32	false	Debner, Anton and Hirvisalo, Vesa	given family Anton Debner given family Vesa Hirvisalo	2025-01-12		Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL)	265	inproceedings	date-parts 2025 1 12	https://raw.githubusercontent.com/mlresearch/v265/main/assets/debner25a/debner25a.pdf