A population-based approach for multi-agent interpretable reinforcement learning

Marco Crespi, Andrea Ferigo, Leonardo Lucio Custode, Giovanni Iacca

Abstract: Highlights•We propose an evolutionary algorithm for multi-agent reinforcement learning.•We consider interpretable models based on decision trees.•We demonstrate the method on a highly dynamic competitive multi-agent task.•Our results show the effective applicability of the proposed method.