Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem ...

Cooperative lane-changing in mixed traffic: a deep reinforcement learning approach

Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automated vehicles (CAVs). The uncertainty of human-driven vehicles (HVs...

Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized...

A Comparative Study of Optimization Models for Condition-Based Maintenance Scheduling of an Aircraft Fleet

Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of...

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

Edge-cloud applications are rapidly prevailing in recent years and pose the challenge of using both resource-strenuous edge devices and elastic cloud resources under dynamic workloads. Efficient resource allocation on edge-cloud jobs via cluster schedulers (e.g. Kubernetes/Volcano scheduler) is essential to guarantee their performance, e.g....

Improving Safety of Vertical Manoeuvres in a Layered Airspace with Deep Reinforcement Learning

Current estimates show that the presence of unmanned aviation is likely to grow exponentially over the course of the next decades. Even with the more conservative estimates, these expected high traffic densities require a re-evaluation of the airspace structure to ensure safe and efficient operations. One structure that scored high on both the...

EdgeTuner: Fast Scheduling Algorithm Tuning for Dynamic Edge-Cloud Workloads and Resources

Edge-cloud jobs are rapidly prevailing in many application domains, posing the challenge of using both resource-strenuous edge devices and elastic cloud resources. Efficient resource allocation on such jobs via scheduling algorithms is essential to guarantee their performance, e.g. latency. Deep reinforcement learning (DRL) is increasingly...

Reinforcement Learning Compensated Filter for Position and Orientation Estimation

Pose estimation provides accurate position and orientation information of the intelligent agents in real time. The accuracy of the estimation directly affects the performance of sequential tasks such as mapping, motion planning, and control. EKF (Extended Kalman Filter) is a standard theory for nonlinear pose estimation by modeling state...