29 citations · 66 across the 15 of their papers we have counts for
5 papers · 1 filter
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Binqi Sun, Mirco Theile, Ziyuan Qin +4
Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement…
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Jichao Chen, Omid Esrafilian, Harald Bayerlein +2
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorit…
Physical Deep Reinforcement Learning Towards Safety Guarantee
Hongpeng Cao, Yanbing Mao, Lui Sha +1
Deep reinforcement learning (DRL) has achieved tremendous success in many complex decision-making tasks of autonomous systems with high-dimensional state and/or action spaces. Howe…
Learning to Generate All Feasible Actions
Mirco Theile, Daniele Bernardini, Raphael Trumpp +3
Modern cyber-physical systems are becoming increasingly complex to model, thus motivating data-driven techniques such as reinforcement learning (RL) to find appropriate control age…
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
Harald Bayerlein, Mirco Theile, Marco Caccamo +1
Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-e…