{"type":"ScholarlyArticle","title":"Measuring Acoustics with Collaborative Multiple Agents","authors":[{"name":"Yinfeng Yu","slug":"yinfeng-yu","given":"Yinfeng","family":"Yu","affiliation":"Beijing National Research Center for Information Science and Technology , State Key Lab on Intelligent Technology and Systems , Department of Computer Science and Technology , Tsinghua University; College of Information Science and Engineering , Xinjiang University"},{"name":"Changan Chen","slug":"changan-chen","given":"Changan","family":"Chen","affiliation":"University of Texas at Austin"},{"name":"Lele Cao","slug":"lele-cao","given":"Lele","family":"Cao","affiliation":"Beijing National Research Center for Information Science and Technology , State Key Lab on Intelligent Technology and Systems , Department of Computer Science and Technology , Tsinghua University; Motherbrain , EQT Group"},{"name":"Fangkai Yang","slug":"fangkai-yang","given":"Fangkai","family":"Yang","affiliation":"Microsoft Research"},{"name":"Fuchun Sun","slug":"fuchun-sun","given":"Fuchun","family":"Sun","affiliation":"Beijing National Research Center for Information Science and Technology , State Key Lab on Intelligent Technology and Systems , Department of Computer Science and Technology , Tsinghua University; THU-Bosch JCML Center"}],"abstract":"As humans, we hear sound every second of our life. The sound we hear is often affected by the acoustics of the environment surrounding us. For example, a spacious hall leads to more reverberation. Room Impulse Responses (RIR) are commonly used to characterize environment acoustics as a function of the scene geometry, materials, and source/receiver locations. Traditionally, RIRs are measured by setting up a loudspeaker and microphone in the environment for all source/receiver locations, which is time-consuming and inefficient. We propose to let two robots measure the environment's acoustics by actively moving and emitting/receiving sweep signals. We also devise a collaborative multi-agent policy where these two robots are trained to explore the environment's acoustics while being rewarded for wide exploration and accurate prediction. We show that the robots learn to collaborate and move to explore environment acoustics while minimizing the prediction error. To the best of our knowledge, we present the very first problem formulation and solution to the task of collaborative environment acoustics measurements with multiple agents.","doi":"10.24963/ijcai.2023/38","url":"https://ijcai.org/proceedings/2023/38","pdf":"https://ijcai.org/proceedings/2023/0038.pdf","markdown":"https://ijcai.org/proceedings/2023/38.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/38","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":38,"pages":{"first":335,"last":343},"keywords":["Game Theory and Economic Paradigms: GTEP: Cooperative games","Agent-based and Multi-agent Systems: MAS: Multi-agent learning"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2016] Chunxiao Cao, Zhong Ren, Carl Schissler, Dinesh Manocha, and Kun Zhou. 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