Towards concurrent real-time audio-aware agents with deep reinforcement learning

Thumbnail Image

Files

Towards_concurrent_real-time_audio-aware_agents_with_deep_reinforcement_learning.pdf (1.57 MB)

Access rights

openAccess
CC BY
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025

Department

Department of Computer Science

Major/Subject

Mcode

Degree programme

Language

en

Pages

9

Series

Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), Volume 265, pp. 32-40, Proceedings of Machine Learning Research ; Volume 265

Abstract

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.

Description

Publisher Copyright: © NLDL 2025.All rights reserved.

Keywords

Other note

Citation

Debner, A & Hirvisalo, V 2025, Towards concurrent real-time audio-aware agents with deep reinforcement learning. in T Lutchyn, A R Rivera & B Ricaud (eds), Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL). vol. 265, Proceedings of Machine Learning Research, vol. 265, JMLR, pp. 32-40, Northern Lights Deep Learning Conference, Tromso, Norway, 07/01/2025. < https://proceedings.mlr.press/v265/debner25a.html >

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202506245248

Collections

[article-cris] Perustieteiden korkeakoulu / SCI