  In this paper we take the first steps in studying a new approach to synthesis
of efficient communication schemes in multi-agent systems, trained via
reinforcement learning. We combine symbolic methods with machine learning, in
what is referred to as a neuro-symbolic system. The agents are not restricted
to only use initial primitives: reinforcement learning is interleaved with
steps to extend the current language with novel higher-level concepts, allowing
generalisation and more informative communication via shorter messages. We
demonstrate that this approach allow agents to converge more quickly on a small
collaborative construction task.
