A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks
Reinforcement learning (RL) has seen an uptick in research interest in recent years, with many papers published in a plethora of different fields, topics and applications. A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10609369/ |
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| author | David J. Richter Ricardo A. Calix Kyungbaek Kim |
| author_facet | David J. Richter Ricardo A. Calix Kyungbaek Kim |
| author_sort | David J. Richter |
| collection | DOAJ |
| description | Reinforcement learning (RL) has seen an uptick in research interest in recent years, with many papers published in a plethora of different fields, topics and applications. A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. This marriage of deep neural networks and traditional reinforcement learning methods has enabled reinforcement learning algorithms to achieve incredible results in use-cases that were previously deemed to be too complex. One of these highly complex scenarios is the realm of flight. In the last few years the number of papers published in this field has increased dramatically, but there is a fairly big divide between two major categories (quadcopters vs. fixed-wing aircraft). There is published work focusing on reinforcement learning using quadcopters, also known as drones, and there is published work focusing on fixed-wing aircraft, the more traditional aircraft, commonly referred to as airplanes. This paper focuses on the latter and will give an overview over the current state of reinforcement learning in the field of fixed-wing aviation. It will look into available toolkits usable for research as well as give an overview of different fields and tasks within reinforcement learning for fixed-wing aircraft control tasks. It contributes by providing detailed information about the methodologies used in each paper, while pointing out each paper’s contributions as well. A total of 48 papers on fixed-wing RL and airplane control are discussed. With the main focus lying on papers published post 2015, after the publication of Mnih et al.’s impactful deep RL paper. |
| format | Article |
| id | doaj-art-a82d19dbed8c4171ba68862d10b943b3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a82d19dbed8c4171ba68862d10b943b32025-01-30T00:01:26ZengIEEEIEEE Access2169-35362024-01-011210302610304810.1109/ACCESS.2024.343354010609369A Review of Reinforcement Learning for Fixed-Wing Aircraft Control TasksDavid J. Richter0https://orcid.org/0000-0001-5413-6710Ricardo A. Calix1Kyungbaek Kim2https://orcid.org/0000-0001-9985-3051Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South KoreaCollege of Technology, Purdue University Northwest, Hammond, IN, USADepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South KoreaReinforcement learning (RL) has seen an uptick in research interest in recent years, with many papers published in a plethora of different fields, topics and applications. A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. This marriage of deep neural networks and traditional reinforcement learning methods has enabled reinforcement learning algorithms to achieve incredible results in use-cases that were previously deemed to be too complex. One of these highly complex scenarios is the realm of flight. In the last few years the number of papers published in this field has increased dramatically, but there is a fairly big divide between two major categories (quadcopters vs. fixed-wing aircraft). There is published work focusing on reinforcement learning using quadcopters, also known as drones, and there is published work focusing on fixed-wing aircraft, the more traditional aircraft, commonly referred to as airplanes. This paper focuses on the latter and will give an overview over the current state of reinforcement learning in the field of fixed-wing aviation. It will look into available toolkits usable for research as well as give an overview of different fields and tasks within reinforcement learning for fixed-wing aircraft control tasks. It contributes by providing detailed information about the methodologies used in each paper, while pointing out each paper’s contributions as well. A total of 48 papers on fixed-wing RL and airplane control are discussed. With the main focus lying on papers published post 2015, after the publication of Mnih et al.’s impactful deep RL paper.https://ieeexplore.ieee.org/document/10609369/Airplanesartificial intelligenceautonomous flightaviationdeep learningdeep reinforcement learning |
| spellingShingle | David J. Richter Ricardo A. Calix Kyungbaek Kim A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks IEEE Access Airplanes artificial intelligence autonomous flight aviation deep learning deep reinforcement learning |
| title | A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks |
| title_full | A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks |
| title_fullStr | A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks |
| title_full_unstemmed | A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks |
| title_short | A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks |
| title_sort | review of reinforcement learning for fixed wing aircraft control tasks |
| topic | Airplanes artificial intelligence autonomous flight aviation deep learning deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10609369/ |
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