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Papers by Francesco Frattolillo

Research paper thumbnail of MULTITTRUST: 2nd Workshop on Multidisciplinary Perspectives on Human-AI Team Trust

Research paper thumbnail of Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

Drones, Mar 28, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Mixed Human-UAV Reinforcement Learning: Literature Review and Open Challenges

Proceedings of the 1st International Conference on Cognitive Aircraft Systems

Research paper thumbnail of Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

Drones

In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has ... more In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of researc...

Research paper thumbnail of Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applica- tions ha... more In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applica-
tions has progressively increased thanks to advancements in multi-agent system technology, which
enables the accomplishment of complex tasks that require cooperative and coordinated abilities.
In this article, multi-UAV applications are grouped into five classes based on their primary task:
coverage,adversarial search and game, computational offloading, communication, and target-driven
navigation. By employing a systematic review approach, we select the most significant works that
use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems
and discuss their features using extensive and constructive critical reasoning. Finally, we present the
most likely and promising research directions by highlighting the limitations of the currently held
assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems.
The suggested areas of research can enhance the transfer of knowledge from simulations to real-world
environments and can increase the responsiveness and safety of UAV systems.

Research paper thumbnail of MULTITTRUST: 2nd Workshop on Multidisciplinary Perspectives on Human-AI Team Trust

Research paper thumbnail of Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

Drones, Mar 28, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Mixed Human-UAV Reinforcement Learning: Literature Review and Open Challenges

Proceedings of the 1st International Conference on Cognitive Aircraft Systems

Research paper thumbnail of Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

Drones

In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has ... more In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applications has progressively increased thanks to advancements in multi-agent system technology, which enables the accomplishment of complex tasks that require cooperative and coordinated abilities. In this article, multi-UAV applications are grouped into five classes based on their primary task: coverage, adversarial search and game, computational offloading, communication, and target-driven navigation. By employing a systematic review approach, we select the most significant works that use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems and discuss their features using extensive and constructive critical reasoning. Finally, we present the most likely and promising research directions by highlighting the limitations of the currently held assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems. The suggested areas of researc...

Research paper thumbnail of Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applica- tions ha... more In recent years, the use of multiple unmanned aerial vehicles (UAVs) in various applica-
tions has progressively increased thanks to advancements in multi-agent system technology, which
enables the accomplishment of complex tasks that require cooperative and coordinated abilities.
In this article, multi-UAV applications are grouped into five classes based on their primary task:
coverage,adversarial search and game, computational offloading, communication, and target-driven
navigation. By employing a systematic review approach, we select the most significant works that
use deep reinforcement learning (DRL) techniques for cooperative and scalable multi-UAV systems
and discuss their features using extensive and constructive critical reasoning. Finally, we present the
most likely and promising research directions by highlighting the limitations of the currently held
assumptions and the constraints when dealing with collaborative DRL-based multi-UAV systems.
The suggested areas of research can enhance the transfer of knowledge from simulations to real-world
environments and can increase the responsiveness and safety of UAV systems.

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