Autonomous navigation is one of the key techniques of the extensive applications of unmanned aerial vehicles (UAVs) in various fields, such as urban traffic management, disaster response, and intelligent logistics. However, 3D cooperative navigation of UAVs in dense urban environments continues to face significant challenges, including extensive state spaces, trajectory smoothness, collaborative operations, and complex motion control. To address these challenges, we propose a distance-attention augmented reinforcement learning (DA2RL) algorithm to enable safe and smooth cooperative navigation of UAVs. In DA2RL, a distance-attention-based actor network is developed to refine the observation space and capture sequential information during training. A historical feature flow-based critic network is then developed for more accurate action decision evaluation. Additionally, several non-sparse reward functions are designed to further accelerate the training process. Finally, numerical experiments and comparison results demonstrate that DA2RL achieves superior navigation performance and generalization capability compared to recent benchmark works.

Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.