Automated Multi-Aircraft Rerouting Under Convective Weather Using Policy-Shared Deep Reinforcement Learning

Automated decision support tools are increasingly important for assisting pilots and air traffic controllers in managing aircraft operations under complex scenarios such as convective weather, especially given the increasing traffic density and the limits of human cognitive capacity. Existing automation methods based on geometric heuristics or optimization are efficient and interpretable, but fail to coordinate multiple aircraft and adapt to rapidly evolving airspace conditions. This research presents a decentralized deep reinforcement learning (DRL) framework for multi-aircraft rerouting in thunderstorm-affected environments. Each aircraft acts as an autonomous agent and learns a shared policy via Independent Deep Deterministic Policy Gradient (IDDPG). During training, agents optimize a shared multi-objective reward that encodes safety and efficiency. By learning from diverse multi-agent scenarios, the shared policy captures transferable coordination patterns, enabling agents to generalize across traffic densities and storm configurations. The evaluation results for both simulated and real world airspace scenarios show that the proposed method reduces the aircraft conflict rates to below 1 %, maintains a success of over 95 % in reaching exit waypoints, and produces smoother trajectories with more organized traffic flows compared to baseline methods. These findings demonstrate the potential of the method as a reliable and scalable AI-based decision support tool for real-time multi-aircraft rerouting in convective weather conditions.