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Evolving Swarm Communication with NEAT Rex

We believe that communication in swarm robotics can be crucial and offer significant improvements in efficiency of certain tasks. Influenced by the paper Comparative Evolutions of Swarm Communications[2] written by previous cs81 students Amy Jin and Murphy Austin, we designed a task where having communication would provide an efficient solution. Inspired by the behaviors of ants collaborating to search and transport food, we created a world where three swarm agents are to search for a food object and all gather around the food. It is important to note that the agents do not have any information about the food besides a sensor that indicates whether the agents have reached food or not. This way, solving this task without communication would mean that the three agents need to individually stumble upon the food. On the other hand, if the agents are capable of communicating, an ideal situation would be that one agent discovers the food and through communication signals the two other agents. We will utilize neural networks and the genetic algorithm NeuroEvolution of Augmenting Topologies (NEAT) to evolve such communication between the agents. Our hypotheses are that not only will all three agents reach the food object, but also effective communication strategies will be evolved so that the agents complete this task efficiently. In addition, we believe that NEAT is powerful enough to make sense of the inputs that we provide and generate meaningful outputs. To test these hypotheses, we created three testing groups: agents that have free communication, fixed communication, and no communication. After gathering the results, we discovered that all three groups were able to come up with strategies that allow them to complete the task, however we did not observe any meaningful communication being evolved. Yet, on a more optimistic note, there was evidence that the agents did react differently when they reached the food object, so this indicated that on some level NEAT still made a difference, and it is our future work to further understand what evolved behaviors our agents possess.