Networked Multi-Agent Reinforcement Learning with Emergent Communication

We develop a Multi-Agent Reinforcement Learning (MARL) method that finds approximately optimal policies for cooperative agents that co-exist in an environment. Central to achieving this is how the agents learn to communicate with each other. Can they together develop a language while learning to perform a common task? We formulate and study a MARL problem where cooperative agents are connected via a fixed underlying network. These agents communicate along the edges of this network by exchanging discrete symbols. However, the semantics of these symbols are not predefined and have to be learned during the training process. We propose a method for training these agents using emergent communication. We demonstrate the applicability of the proposed framework by applying it to the problem of managing traffic controllers, where we achieve state-of-the-art performance (as compared to several strong baselines) and perform a detailed analysis of the emergent communication.