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Learning to Generate All Feasible Actions

We introduce action mapping, a novel approach that divides the learning process into two steps: first learn feasibility and subsequently, the objective by mapping actions into the sets of feasible actions.

Mirco Theile, Daniele Bernardini, Raphael Trumpp, Cristina Piazza, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli

Learning to Generate All Feasible Actions

Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning

This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability. Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling – EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint.

Binqi Sun, Mirco Theile, Ziyuan Qin, Daniele Bernardini, Debayan Roy, Andrea Bastoni, Marco Caccamo

Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning

A Cyber-Physical Prototyping and Testing Framework to Enable the Rapid Development of UAVs

In this work, a cyber-physical prototyping and testing framework to enable the rapid development of UAVs is conceived and demonstrated. …

Or D. Dantsker, Mirco Theile, Marco Caccamo

Multi-UAV Path Planning for Wireless Data Harvesting With Deep Reinforcement Learning

We propose to train a DDQN agent based on global and local map data to learn collaborative decentralized multi-agent policies for UAV data harvesting missions.

Harald Bayerlein, Mirco Theile, Marco Caccamo, David Gesbert