The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange. Communication with discrete messages is considered a desired characteristic, for both scientific and applied reasons. However, training a multi-agent system with discrete communication is not straightforward, requiring either reinforcement learning algorithms or relaxing the discreteness requirement via a continuous approximation such as the Gumbel-softmax. Both these solutions result in poor performance compared to fully continuous communication. In this work, we propose an alternative approach to achieve discrete communication -- quantization of communicated messages. Using message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups. Moreover, quantization is a natural framework that runs the gamut from continuous to discrete communication. Thus, it sets the ground for a broader view of multi-agent communication in the deep learning era.
翻译:新兴通信领域的目的是了解通信的特征,因为它产生于需要信息交流的任务的人工代理物。与离散信息进行通信被视为一个理想的特征,无论是科学原因还是应用原因。然而,对具有离散通信的多试剂系统进行培训并非直截了当,既需要强化学习算法,也需要通过连续近似(如Gumbel-Softmax)放松离散要求。这两种解决方案都导致与完全连续通信相比的性能不佳。在这项工作中,我们提出了实现离散通信的替代方法,即对传递的信息进行量化。使用信息量化,使我们能够培训模式端到端,在多个设置中实现优异性。此外,量化是一个从连续通信到离散通信的自然框架。因此,它为深入学习的多试剂通信提供了更广阔的视野。