Zobrazeno 1 - 10
of 421
pro vyhledávání: '"Safe reinforcement learning"'
Publikováno v:
Безопасность информационных технологий, Vol 31, Iss 2, Pp 90-110 (2024)
Striking a balance between safety and performance remains a critical concern, despite advancements in the field. To address this issue, a versatile framework named Safety Goes Along with Performance (SGAWP) is proposed, centered on off-policy algorit
Externí odkaz:
https://doaj.org/article/723b9a9315634d5e981dd3abef70ba8f
Autor:
Sajad Roshanravan, Saeed Shamaghdari
Publikováno v:
مکانیک هوافضا, Vol 20, Iss 1, Pp 143-162 (2024)
In this article, a method for designing a fault-tolerant optimal attitude tracking control (FTOATC) for a quadrotor UAV subject to component and actuator faults is presented. The proposed fault-tolerant method is based on safe reinforcement learning
Externí odkaz:
https://doaj.org/article/44ed604f2cbf4c3f87e0b90eac6f3d27
Publikováno v:
Advances in Applied Energy, Vol 15, Iss , Pp 100183- (2024)
The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain uncl
Externí odkaz:
https://doaj.org/article/8757d769550b4cc19a2d034967025b4d
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena September 2024 186
Autor:
Afriyadi Afriyadi, Wiranto Herry Utomo
Publikováno v:
Jurnal Sisfokom, Vol 12, Iss 3, Pp 378-393 (2023)
Reinforcement learning (RL) is a powerful tool for training agents to perform complex tasks. However, from time-to-time RL agents often learn to behave in unsafe or unintended ways. This is especially true during the exploration phase, when the agent
Externí odkaz:
https://doaj.org/article/d523a4fab3074bd18d5c608692193d9d
Publikováno v:
In Expert Systems With Applications 1 April 2025 267
Autor:
Changquan Wang, Yun Wang
Publikováno v:
Sensors, Vol 24, Iss 10, p 3139 (2024)
Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in c
Externí odkaz:
https://doaj.org/article/6c318132e67c4ed4a435da53cb6aba66
Publikováno v:
Machines, Vol 12, Iss 4, p 252 (2024)
Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perfor
Externí odkaz:
https://doaj.org/article/6eb4fe15619340c1ace0851248c2e53f
Publikováno v:
Robotics, Vol 13, Iss 4, p 63 (2024)
In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible
Externí odkaz:
https://doaj.org/article/70e20e8101234cefa78cc67f97ab2884
Autor:
Jinming Xu, Yuan Lin
Publikováno v:
Mathematics, Vol 12, Iss 5, p 663 (2024)
Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial
Externí odkaz:
https://doaj.org/article/b39a17237bda4a02b2f5b96300d61f3a