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Acquiring your first language is an incredible feat and not easilyduplicated. Learning to communicate using nothing but a few pictureless books,a corpus, would likely be impossible even for humans. Nevertheless, this is thedominating approach in most natural language processing today. As analternative, we propose the use of situated interactions between agents as adriving force for communication, and the framework of Deep Recurrent Q-Networksfor evolving a shared language grounded in the provided environment. We taskthe agents with interactive image search in the form of the game Guess Who?.The images from the game provide a non trivial environment for the agents todiscuss and a natural grounding for the concepts they decide to encode in theircommunication. Our experiments show that the agents learn not only to encodephysical concepts in their words, i.e. grounding, but also that the agentslearn to hold a multi-step dialogue remembering the state of the dialogue fromstep to step.

Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence