Distributed Embodied Evolution in Networks of Agents

In most network problems, the optimum behaviors of agents in the network are not known before deployment. In addition to that, agents might be required to adapt, i.e. change their behavior based on the environment conditions. In these scenarios, offline optimization is usually costly and inefficient, while online methods might be more suitable. In this work we propose a distributed embodied evolutionary approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment. Our numerical results show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to converge to the global optimum more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behaviors within a neighborhood.