Emergent Quantized Communication

• URL: http://arxiv.org/abs/2211.02412v1
• Date: Fri, 4 Nov 2022 12:39:45 GMT
• Title: Emergent Quantized Communication
• Authors: Boaz Carmeli, Ron Meir, Yonatan Belinkov
• Abstract summary: We propose an alternative approach to achieve discrete communication -- quantization of communicated messages. Message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups.
• Score: 34.31732248872158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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