Modeling Dynamic Obstacle Avoidance Strategy of Drone Swarms Combined with Multi-Agent Reinforcement Learning

Abstract

This paper proposes the Locally-decoupled and Embedding-enhanced Multi-Agent Deep Deterministic Policy Gradient (LDE-MADDPG) algorithm to address poor scalability and delayed response in drone swarm dynamic obstacle avoidance under complex cooperative environments. Such autonomous coordination capabilities are also important for distributed sensing, wireless networking, and electromagnetic information exchange in future intelligent aerial systems. The algorithm introduces three key innovations beyond standard MADDPG: a Graph Attention Network module that encodes variable-length observations into fixed-dimensional embeddings for swarm-size generalization; a dual-path critic with a global branch guiding policy updates and a local branch specializing in obstacle avoidance evaluation; and a hierarchical reward integrating multi-objective signals. Evaluated across eight static and dynamic obstacle scenarios, LDE-MADDPG achieves significantly lower collision rates (2.1%–4.2% in static scenarios and 3.8%–7.2% in dynamic scenarios) than state-of-the-art baselines and reaches a 97.5% mission completion rate in 100 random scenarios. The proposed framework demonstrates robust scalability and real-time coordination capability for dynamic environments, while providing a reliable decision-making paradigm for intelligent multi-agent systems operating in communication-intensive and electromagnetically complex application scenarios.