  We study emergent communication in a multi-agent reinforcement learning
setting, where the agents solve cooperative tasks and have access to a
communication channel. The communication channel may consist of either discrete
symbols or continuous variables. We introduce an inductive bias to aid with the
emergence of good communication protocols for continuous messages, and we look
at the effect this type of inductive bias has for continuous and discrete
messages in itself or when used in combination with reinforcement learning. We
demonstrate that this type of inductive bias has a beneficial effect on the
communication protocols learnt in two toy environments, Negotiation and
Sequence Guess.

Machine Learning;Artificial Intelligence;Multiagent Systems