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Wyszukujesz frazę "adaptive policy" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Nonparametric adaptive control for discrete-time Markov processes with unbounded costs under average criterion
Autorzy:
Minjárez-Sosa, J.
Tematy:
Markov control process
discounted and average cost criterion
adaptive policy
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Data publikacji:
1999
Powiązania:
https://bibliotekanauki.pl/articles/1338768.pdf  Link otwiera się w nowym oknie
Źródło:
Applicationes Mathematicae; 1999, 26, 3; 267-280
1233-7234
Pojawia się w:
Applicationes Mathematicae
Opis:
We introduce average cost optimal adaptive policies in a class of discrete-time Markov control processes with Borel state and action spaces, allowing unbounded costs. The processes evolve according to the system equations $x_{t+1}=F(x_t,a_t,ξ _t)$, t=1,2,..., with i.i.d. $ℝ^k$-valued random vectors $ξ_t$, which are observable but whose density ϱ is unknown.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Call Blocking Probabilities of Multirate Elastic and Adaptive Traffic under the Threshold and Bandwidth Reservation Policies
Autorzy:
Moscholios, I. D.
Logothetis, M. D.
Boucouvalas, A. C.
Vassilakis, V. G.
Tematy:
adaptive trac policy
Call Blocking Probabilities
Multirate Loss Model
threshold and bandwidth reservation policy
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Data publikacji:
2016
Powiązania:
https://bibliotekanauki.pl/articles/308771.pdf  Link otwiera się w nowym oknie
Źródło:
Journal of Telecommunications and Information Technology; 2016, 1; 44-52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Opis:
This paper proposes multirate teletraffic loss models of a link that accommodates different service-classes of elastic and adaptive calls. Calls follow a Poisson process, can tolerate bandwidth compression and have an exponentially distributed service time. When bandwidth compression occurs, the service time of new and in-service elastic calls increases. Adaptive calls do not alter their service time. All calls compete for the available link bandwidth under the combination of the Threshold (TH) and the Bandwidth Reservation (BR) policies. The TH policy can provide different QoS among service-classes by limiting the number of calls of a service-class up to a predefined threshold, which can be different for each service-class. The BR policy reserves part of the available link bandwidth to benefit calls of high bandwidth requirements. The analysis of the proposed models is based on approximate but recursive formulas, whereby authors determine call blocking probabilities and link utilization. The accuracy of the proposed formulas is verified through simulation and found to be very satisfactory.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing a controller for an adaptive cruise control (ACC) system: utilizing deep reinforcement learning (DRL) approach
Autorzy:
Rizehvandi, Ali
Azadi, Shahram
Tematy:
adaptive cruise control system
deep reinforcement learning method
deep deterministic policy gradient algorithm
tempomat adaptacyjny
uczenie głębokie ze wzmocnieniem
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Data publikacji:
2024
Powiązania:
https://bibliotekanauki.pl/articles/58909546.pdf  Link otwiera się w nowym oknie
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2024, 125; 243-257
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Opis:
The addition of Adaptive Cruise Control (ACC) to vehicles enables automatic speed adjustments based on traffic conditions after the driver sets the maximum speed, freeing them to concentrate on steering. This study is dedicated to the development of a passenger car ACC system using Deep Reinforcement Learning (DRL). A critical aspect of this ACC system is its capability to regulate the distance between vehicles by taking into account preceding and following vehicle speeds. It considers three primary inputs: the memory-stored speed of the following vehicle, the lead time specified by the driver, and the radar-measured distance. By adapting speed in different traffic scenarios, the system contributes to averting potential accidents. This research delves into constructing a controller that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm and compares its outcomes with those from the DQN algorithm. The DDPG controller supervises the longitudinal control actions of a vehicle, enabling it to execute stopping and moving maneuvers safely and efficiently.
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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