Multi-agent collaboration with ergodic spatial distributions

When considering collaboration among agents in multi-agent systems, individual and team measures of performance are used to describe the collaboration. Typically, the definition of collaboration is limited in that it is only indicative of coordination required for a small class of tasks wherein this coordination is necessary for task completion (e.g. two or more agents needed to lift a heavy object). In this work, we aim to present a method that may be used to classify individual and group behaviors, enabling the measurement of collaboration among agents. We demonstrate the capability to use performance and behavioral data from computational learning agents in a predator-prey pursuit task to produce ergodic spatial distributions. Ergodicity is shown quantitatively and used to benchmark performance. The ergodic distributions shown, reflect the learned policies developed through multi-agent reinforcement learning (MARL). We also demonstrate that independently trained models produce distinctly different behavior, as revealed through ergodic spatial distributions. The ergodicity of the agents’ behavior provides both a potential path for classifying group behavior, predicting performance of group behavior with novel partners, and a quantifiable measure of collaboration built from explicitly aligned goals (i.e., cooperation) as a result of behavioral interdependencies.