Decentralized Deep Reinforcement Learning for Cooperative Multi-Agent Flight Trajectory Planning in Adverse Weather

Pang, Bizhao and Hu, Xinting and Zhang, Mingcheng and Alam, Sameer and Lulli, Guglielmo (2025) Decentralized Deep Reinforcement Learning for Cooperative Multi-Agent Flight Trajectory Planning in Adverse Weather. In: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025) :. ACM, New York, pp. 2705-2707. ISBN 9798400714269

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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