Decentralized deep reinforcement learning for cooperative multi-agent flight trajectory planning in adverse weather

Abstract

Adverse weather, especially thunderstorms, disrupts air traffic operations and requires real-time trajectory adjustments to ensure aircraft safety. Existing methods often rely on centralized or single agent approaches, lacking the coordination and robustness needed for scalable solutions. This paper presents a decentralized multiagent method for cooperative trajectory planning, where each aircraft operates as an autonomous agent. The problem is modeled as a Decentralized Markov Decision Process (DEC-MDP) and solved with a proposed Independent Deep Deterministic Policy Gradient (IDDPG) algorithm. Experimental results show that the proposed method outperforms the state-of-the-art baselines in maintaining safe separation and optimizing rerouting efficiency under dynamically evolving thunderstorm cells.