Reinforcement learning–based task allocation and path‐finding in multi‐robot systems under environment uncertainty
Songjun Huang, Chuanneng Sun, Jie Gong, Dario Pompili · Computer-Aided Civil and Infrastructure Engineering · 2025
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.