Exploration Techniques in Reinforcement Learning for Autonomous Vehicles
Autonomous vehicles (AVs) have the potential to revolutionize the transportation system by enhancing road safety, reducing traffic congestion, and freeing drivers from monotonous tasks. Effective exploration is essential for AVs to navigate safely and adapt to dynamic environments. Reinforcement lea...
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MDPI AG
2024-11-01
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| author | Ammar Khaleel Áron Ballagi |
| author_facet | Ammar Khaleel Áron Ballagi |
| author_sort | Ammar Khaleel |
| collection | DOAJ |
| description | Autonomous vehicles (AVs) have the potential to revolutionize the transportation system by enhancing road safety, reducing traffic congestion, and freeing drivers from monotonous tasks. Effective exploration is essential for AVs to navigate safely and adapt to dynamic environments. Reinforcement learning (RL) enables AVs to learn optimal behaviors through continuous interaction with their environment. This paper reviews recent RL research on designing exploration strategies for single- and multi-agent AV systems. It categorizes exploration methods based on underlying principles and addresses the challenges. It analyzes key RL algorithms’ strengths, limitations, and empirical performance. By compiling and analyzing the current state of research, this paper aims to facilitate future advancements in AV exploration using RL, offering insights into current trends and future directions in this evolving field. |
| format | Article |
| id | doaj-art-dde638fbd7fc470e874b84048e833c8e |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-dde638fbd7fc470e874b84048e833c8e2025-08-20T02:55:37ZengMDPI AGEngineering Proceedings2673-45912024-11-017912410.3390/engproc2024079024Exploration Techniques in Reinforcement Learning for Autonomous VehiclesAmmar Khaleel0Áron Ballagi1Doctoral School of Multidisciplinary Engineering Sciences, Széchenyi István University, Egyetem tér 1, H-9026 Győr, HungaryDepartment of Automation, Széchenyi István University, Egyetem tér 1, H-9026 Győr, HungaryAutonomous vehicles (AVs) have the potential to revolutionize the transportation system by enhancing road safety, reducing traffic congestion, and freeing drivers from monotonous tasks. Effective exploration is essential for AVs to navigate safely and adapt to dynamic environments. Reinforcement learning (RL) enables AVs to learn optimal behaviors through continuous interaction with their environment. This paper reviews recent RL research on designing exploration strategies for single- and multi-agent AV systems. It categorizes exploration methods based on underlying principles and addresses the challenges. It analyzes key RL algorithms’ strengths, limitations, and empirical performance. By compiling and analyzing the current state of research, this paper aims to facilitate future advancements in AV exploration using RL, offering insights into current trends and future directions in this evolving field.https://www.mdpi.com/2673-4591/79/1/24autonomous vehicles explorationexploration vs. exploitationexploration strategies |
| spellingShingle | Ammar Khaleel Áron Ballagi Exploration Techniques in Reinforcement Learning for Autonomous Vehicles Engineering Proceedings autonomous vehicles exploration exploration vs. exploitation exploration strategies |
| title | Exploration Techniques in Reinforcement Learning for Autonomous Vehicles |
| title_full | Exploration Techniques in Reinforcement Learning for Autonomous Vehicles |
| title_fullStr | Exploration Techniques in Reinforcement Learning for Autonomous Vehicles |
| title_full_unstemmed | Exploration Techniques in Reinforcement Learning for Autonomous Vehicles |
| title_short | Exploration Techniques in Reinforcement Learning for Autonomous Vehicles |
| title_sort | exploration techniques in reinforcement learning for autonomous vehicles |
| topic | autonomous vehicles exploration exploration vs. exploitation exploration strategies |
| url | https://www.mdpi.com/2673-4591/79/1/24 |
| work_keys_str_mv | AT ammarkhaleel explorationtechniquesinreinforcementlearningforautonomousvehicles AT aronballagi explorationtechniquesinreinforcementlearningforautonomousvehicles |