Evolving recurrent neural networks for emergent communication.

Recent research showed that deep neural networks can be trained to create shared languages to communicate and cooperate with each other. These approaches used fixed, handcrafted network architectures which were trained with reinforcement learning. We extend this approach by using neuroevolution to automate network design and find network weights of communicating agents. We show that neuroevolution is a viable approach for training agents to develop novel languages so as to communicate amongst themselves.