Reinforcement Learning Compensated Filter for Multi-Agents Cooperative Localization

Abstract

Accurate and real-time location tracking is vital for various applications in public safety and the military, particularly in search and rescue missions. Traditional filtering localization algorithms are more effective in linear environments and require precise initial estimates and system noise for optimal results. In complex and unreliable environments, these algorithms often yield poor localization results. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy of the localization algorithm. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. This approach reduces the system’s positioning errors and addresses credit allocation issues common in multi-agent reinforcement learning.