Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input



Lazaridou, Angeliki, Hermann, Karl Moritz, Tuyls, Karl and Clark, Stephen
(2018) Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input. .

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Abstract

The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks. Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games. We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation. We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.

Item Type: Conference or Workshop Item (Unspecified)
Additional Information: To appear at ICLR 2018
Uncontrolled Keywords: cs.AI, cs.AI, cs.CL, cs.LG, cs.MA
Depositing User: Symplectic Admin
Date Deposited: 07 Dec 2018 15:07
Last Modified: 19 Jan 2023 01:09
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3029651