Self-Attention-Enhanced Multiagent Deep Reinforcement Learning for UAVs Target Search in Obstacle-Dense Environments

In the field of Unmanned Aerial Vehicles(UAVs) decision-making, cooperative target search by multiple UAVs rep resents a central research focus. Recent advancements in neural network-based deep reinforcement learning, coupled with improvements in UAV performance, have significantly enhanced the intelligent decision-making capabilities of UAVs in unknown and dynamic environments. However, as search scenarios grow in scale and complexity, existing UAV search methods increasingly struggle to maintain efficient search performance. To address these challenges, this study introduces a Multi-Agent Deep Deterministic Policy Gradient integrated with a self-attention mechanism (SA MADDPG) algorithm which integrates a self-attention mechanism to facilitate global spatial information modeling. By effectively capturing long-range dependencies within complex, high-dimensional spatial data, such as obstacle distributions, teammate positions, and target probabilities, the algorithm overcomes the constraints of localized receptive fields inherent in conventional architectures. This enables agents to prioritize critical environmental features, thereby significantly enhancing collaborative decision-making in obstacle-dense settings. Furthermore, we develop a dual-incentive exploration mechanism that synthesizes Bayesian-updated target probability maps with Voronoi-region partitioning to enhance multi UAV collaborative coordination and search reliability. Experimental results reveal that the proposed SA-MADDPG method significantly outperforms existing search strategies across critical metrics, including collision rate, coverage rate, and target detection rate.