Zobrazeno 1 - 10
of 256
pro vyhledávání: '"meta-reinforcement learning"'
Publikováno v:
Radioengineering, Vol 33, Iss 3, Pp 417-431 (2024)
Unmanned Aerial Vehicle (UAV) communication networks are vulnerable to malicious jamming and co-channel interference, deteriorating the performance of the networks. Therefore, the exploration of anti-jamming methods to enhance communication security
Externí odkaz:
https://doaj.org/article/4d54647b7d4444d2b8de37eb672ee0fd
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 162, Iss , Pp 110273- (2024)
The integration of renewable energy into the power grid poses significant challenges for optimization and scheduling of the power system. In recent years, methods based on deep reinforcement learning have surpassed traditional methods on the high com
Externí odkaz:
https://doaj.org/article/e2ec9e3919f844b398f8e7fe7f4bb9bd
Publikováno v:
IEEE Open Journal of the Communications Society, Vol 5, Pp 2145-2163 (2024)
Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their potential applications in emergency-response applications, including wireless power transfer (WPT) and data collection from Internet of Things (Io
Externí odkaz:
https://doaj.org/article/b4554a1be13e4b5a867f5c62a32c3b67
Publikováno v:
Applied Sciences, Vol 14, Iss 23, p 10821 (2024)
Reinforcement learning algorithms usually focus on a specific task, which often performs well only in the training environment. When the task changes, its performance drops significantly, with the algorithm lacking the ability to adapt to new environ
Externí odkaz:
https://doaj.org/article/50d5744a966b4ddfaa9790d44a5abd03
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and
Externí odkaz:
https://doaj.org/article/0ad1886aed0745d2bc39b66cd972d300
Autor:
HAN Xu, WU Feng
Publikováno v:
Jisuanji kexue yu tansuo, Vol 17, Iss 8, Pp 1917-1927 (2023)
Traditional reinforcement learning algorithms require lots of online interaction with the environment for training and cannot effectively adapt to changes in the task environment, making them difficult to apply to real-world problems. Offline meta-re
Externí odkaz:
https://doaj.org/article/71335025c9734a6c82d2c2da77e27912
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-12 (2023)
Abstract Task scheduling is a complex problem in cloud computing, and attracts many researchers’ interests. Recently, many deep reinforcement learning (DRL)-based methods have been proposed to learn the scheduling policy through interacting with th
Externí odkaz:
https://doaj.org/article/c716ff3a6f6343628fb477bb7b637fb6
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3209 (2024)
When agents need to collaborate without previous coordination, the multi-agent cooperation problem transforms into an ad hoc teamwork (AHT) problem. Mainstream research on AHT is divided into type-based and type-free methods. The former depends on kn
Externí odkaz:
https://doaj.org/article/b5a6e7ba82bd4f8ea55baa21895d0248
Publikováno v:
Future Internet, Vol 16, Iss 3, p 105 (2024)
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitation
Externí odkaz:
https://doaj.org/article/5201137c8987492b993a361b4cfccdc5
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