Reinforcement learning with function approximation for cooperative navigation tasks

Abstract

In this paper, we propose a reinforcement learning approach to address multi-robot cooperative navigation tasks in infinite settings. We propose an algorithm to simultaneously address the problems of learning and coordination in multi-robot problems. The proposed algorithm extends those existing in the literature, allowing to address simultaneous learning and coordination in problems with an infinite state-space. We also present the results obtained in several test scenarios featuring multi-robot navigation situations with partial observability.