Unmanned Aerial Vehicle (UAV) 'Drones' using Machine Learning

6 Pages Posted: 10 May 2023

See all articles by Ummey Habiba

Ummey Habiba

Integral University

Roshan Jahan

Integral University

Date Written: April 3, 2023


Unmanned aerial vehicle decision-making issues are increasingly being addressed using reinforcement learning (RL) (UAVs). The current advances in RL-based algorithms for UAV applications, encompassing both single-agent and swarm scenarios, are thoroughly reviewed in this work. First, the basic concepts of RL and its variants are introduced, followed by an overview of the state-of-the art RL algorithms that have been applied to UAV navigation, path planning, and obstacle avoidance. The study then examines real-time learning concerns, model selection, and exploration exploitation trade-offs, as well as challenges and potential for employing RL in UAV systems. In order to further the use of RL in UAVs, future research initiatives are also suggested. They include creating hybrid methods that integrate RL with other methodologies and incorporating human feedback and domain expertise into the learning process. Overall, this work demonstrates the potential of this approach to improve the autonomy, adaptability, and resilience of UAV systems and serves as a significant resource for researchers and those interested in applying RL to UAVs.

Keywords: UAV, Autonomy, Reinforcement, Aircraft Vehicle

Suggested Citation

Habiba, Ummey and Jahan, Roshan, Unmanned Aerial Vehicle (UAV) 'Drones' using Machine Learning (April 3, 2023). Available at SSRN: https://ssrn.com/abstract=4430575 or http://dx.doi.org/10.2139/ssrn.4430575

Ummey Habiba (Contact Author)

Integral University ( email )

Roshan Jahan

Integral University

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