Reinforcement learning–based task allocation and path‐finding in multi‐robot systems under environment uncertainty

Abstract

Autonomous robots have the potential to significantly improve the operational efficiency of multi‐robot systems (MRSs) under environment uncertainties. Achieving robust performance in these settings requires effective task allocation and adaptive path‐finding. However, conventional model‐based frameworks often rely on centralized control or global information, making them impractical when communication is intermittent or maps are unavailable. Although recent studies have shown that reinforcement learning (RL)‐based frameworks offer improved performance, problems related to synchronization and adaptability in diverse environments remain unresolved. To address these problems, this study proposes the “RL‐based Task‐Allocation and Path‐Finding under Uncertainty (RL‐TAPU)” framework. This framework incorporates an Action‐Selective Double‐Q‐Learning (ASDQ) algorithm for real‐time task allocation and a Context‐Aware Meta‐Q‐Learning (CA‐MQL) algorithm for adaptive path‐finding. Unlike previous RL‐based frameworks, RL‐TAPU is designed to operate without global maps, uses only local state information, and functions reliably under intermittent and low‐bandwidth communication conditions. The task allocator communicates only minimal information, and the path‐finding component adapts to new environments without the need for complete environmental data. Experimental results show that the RL‐TAPU framework achieves better adaptability and works more efficiently with a shorter total execution time than competitors.