SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Li Zhiwu) srt2:(2023)"

Sökning: WFRF:(Li Zhiwu) > (2023)

  • Resultat 1-2 av 2
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • Yang, Junjun, et al. (författare)
  • A model-based deep reinforcement learning approach to the nonblocking coordination of modular supervisors of discrete event systems
  • 2023
  • Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 630, s. 305-321
  • Tidskriftsartikel (refereegranskat)abstract
    • Modular supervisory control may lead to conflicts among the modular supervisors for large-scale discrete event systems. The existing methods for ensuring nonblocking control of modular supervisors either exploit favorable structures in the system model to guarantee the nonblocking property of modular supervisors or employ hierarchical model abstraction methods for reducing the computational complexity of designing a nonblocking coordinator. The nonblocking modular control problem is, in general, NP-hard. This study integrates supervisory control theory and a model-based deep reinforcement learning method to synthesize a nonblocking coordinator for the modular supervisors. The deep reinforcement learning method significantly reduces the computational complexity by avoiding the computation of synchronization of multiple modular supervisors and the plant models. The supervisory control function is approximated by the deep neural network instead of a large-sized finite automaton. Furthermore, the proposed model-based deep reinforcement learning method is more efficient than the standard deep Q network algorithm.
  •  
2.
  • Yang, Junjun, et al. (författare)
  • Reducing the Learning Time of Reinforcement Learning for the Supervisory Control of Discrete Event Systems
  • 2023
  • Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 59840-59853
  • Tidskriftsartikel (refereegranskat)abstract
    • Reinforcement learning (RL) can obtain the supervisory controller for discrete-event systems modeled by finite automata and temporal logic. The published methods often have two limitations. First, a large number of training data are required to learn the RL controller. Second, the RL algorithms do not consider uncontrollable events, which are essential for supervisory control theory (SCT). To address the limitations, we first apply SCT to find the supervisors for the specifications modeled by automata. These supervisors remove illegal training data violating these specifications and hence reduce the exploration space of the RL algorithm. For the remaining specifications modeled by temporal logic, the RL algorithm is applied to search for the optimal control decision within the confined exploration space. Uncontrollable events are considered by the RL algorithm as uncertainties in the plant model. The proposed method can obtain a nonblocking supervisor for all specifications with less learning time than the published methods.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-2 av 2
Typ av publikation
tidskriftsartikel (2)
Typ av innehåll
refereegranskat (2)
Författare/redaktör
Feng, Lei (2)
Li, Zhiwu (2)
Tan, Kaige (2)
Yang, Junjun (2)
El-Sherbeeny, Ahmed ... (1)
Lärosäte
Kungliga Tekniska Högskolan (2)
Språk
Engelska (2)
Forskningsämne (UKÄ/SCB)
Teknik (2)
År
2023 (2)Ta bort avgränsningen

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy