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Multi-agent reinforcement learning has been used as an effective means tostudy emergent communication between agents, yet little focus has been given tocontinuous acoustic communication. This would be more akin to human languageacquisition; human infants acquire language in large part through continuoussignalling with their caregivers. We therefore ask: Are we able to observeemergent language between agents with a continuous communication channel? Ourgoal is to provide a platform to begin bridging the gap between human and agentcommunication, allowing us to analyse continuous signals, how they emerge,their characteristics, and how they relate to human language acquisition. Wepropose a messaging environment where a Speaker agent needs to convey a set ofattributes to a Listener over a noisy acoustic channel. Using DQN to train ouragents, we show that: (1) unlike the discrete case, the acoustic Speaker learnsredundancy to improve Listener coherency, (2) the acoustic Speaker developsmore compositional communication protocols which implicitly compensates fortransmission errors over a noisy channel, and (3) DQN has significantperformance gains and increased compositionality when compared to previousmethods optimised using REINFORCE.

Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel