Emergent Communication with World Models

• URL: http://arxiv.org/abs/2002.09604v1
• Date: Sat, 22 Feb 2020 02:34:51 GMT
• Title: Emergent Communication with World Models
• Authors: Alexander I. Cowen-Rivers, Jason Naradowsky
• Abstract summary: We introduce Language World Models, a class of language-conditional generative model which interpret natural language messages. We incorporate this "observation" into a persistent memory state, and allow the listening agent's policy to condition on it. We show this improves effective communication and task success in 2D gridworld speaker-listener navigation tasks.
• Score: 80.55287578801008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Language World Models, a class of language-conditional generative model which interpret natural language messages by predicting latent codes of future observations. This provides a visual grounding of the message, similar to an enhanced observation of the world, which may include objects outside of the listening agent's field-of-view. We incorporate this "observation" into a persistent memory state, and allow the listening agent's policy to condition on it, akin to the relationship between memory and controller in a World Model. We show this improves effective communication and task success in 2D gridworld speaker-listener navigation tasks. In addition, we develop two losses framed specifically for our model-based formulation to promote positive signalling and positive listening. Finally, because messages are interpreted in a generative model, we can visualize the model beliefs to gain insight into how the communication channel is utilized.

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