Multi-agent reinforcement learning has been used as an effective means to study emergent communication between agents, yet little focus has been given to continuous acoustic communication. This would be more akin to human language acquisition; human infants acquire language in large part through continuous signalling with their caregivers. We therefore ask: Are we able to observe emergent language between agents with a continuous communication channel? Our goal is to provide a platform to begin bridging the gap between human and agent communication, allowing us to analyse continuous signals, how they emerge, their characteristics, and how they relate to human language acquisition. We propose a messaging environment where a Speaker agent needs to convey a set of attributes to a Listener over a noisy acoustic channel. Using DQN to train our agents, we show that: (1) unlike the discrete case, the acoustic Speaker learns redundancy to improve Listener coherency, (2) the acoustic Speaker develops more compositional communication protocols which implicitly compensates for transmission errors over a noisy channel, and (3) DQN has significant performance gains and increased compositionality when compared to previous methods optimised using REINFORCE.

翻译：多智能体强化学习已被用作研究智能体间涌现通信的有效手段，但针对连续声学通信的关注仍相对不足。这更接近人类语言习得过程：人类婴儿在很大程度上通过与看护者的连续信号互动来获取语言。因此我们提出疑问：能否在具有连续通信通道的智能体间观察到涌现语言？我们的目标是构建一个平台，以弥合人类与智能体通信之间的鸿沟，从而分析连续信号的涌现过程、特征及其与人类语言习得的关联。我们提出一种消息传递环境：说话者智能体需通过带噪声学通道向听者智能体传递一组属性。采用深度Q网络（DQN）训练智能体后，我们发现：（1）与离散情况不同，声学说话者通过学习冗余信息提升听者解码连贯性；（2）声学说话者开发出更具组合性的通信协议，从而隐式补偿带噪通道中的传输错误；（3）与先前基于REINFORCE算法优化的方法相比，DQN在性能增益与组合性方面均取得显著提升。