DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient...

Creation of memory–memory entanglement in a metropolitan quantum network

Towards realizing the future quantum internet, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive multi-node set-ups on large scales. Here we report the creation of memory–memory entanglement in a multi-node quantum network over a metropolitan area. We use three...

Effects of biological activated carbon filter running time on disinfection by-product precursor removal

Biological activated carbon (BAC) filtration is usually considered to be able to decrease formation potentials (FPs) of disinfection by-products (DBPs) in drinking water treatment plant (DWTP). However, BAC filters with long running time may release microbial metabolites to effluents and therefore increase FPs of nitrogenous DBPs with high...

Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

Safety analysis according to the spatial distribution characteristics of maritime traffic accidents is critical to maritime traffic safety management. An accident analysis framework based on the geographic information system (GIS) is proposed to characterize the spatial distribution of maritime traffic accidents occurring in the Fujian sea area...

Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with a magnetometer. Lyapunov’s method in control theory is employed to prove the convergence of orientation estimation errors. The estimator gains and a Lyapunov function are parametrised by deep neural networks and...