Siny, Xingjian Zhang

This work introduces a multi-agent deep reinforcement learning (MARL) framework for persistent monitoring and informative path planning (IPP) of dynamically evolving targets using unmanned agents. Addressing the challenges inherent in real-time multi-target tracking within uncertain environments, we propose a novel spatio-temporal attention network that integrates Gaussian Processes (GPs) for modeling and predicting target distributions. The developed approach discretizes the monitoring environment into graph structures, enabling agents to leverage historical spatial and temporal information efficiently. Our MARL approach employs a Proximal Policy Optimization (PPO) algorithm within a Centralized Training Decentralized Execution (CTDE) paradigm, ensuring effective cooperation among agents while maintaining autonomy in decision-making during execution. We further introduce an adaptive reward mechanism based on uncertainty reduction, explicitly penalizing redundant observations to encourage diversified exploration and cooperative behavior. Experimental results demonstrate that our method significantly improves information gain and reduces uncertainty in dynamic, multi-target scenarios, showcasing superior collaboration capabilities among agents compared to baseline methods.