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Wyszukujesz frazę "reinforcement learning" wg kryterium: Temat

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method
    Autorzy:
    Pajchrowski, T.
    Siwek, P.
    Wójcik, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/201068.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Reinforcement Learning
    adaptive control
    electric drive
    machine learning
    Opis:
    The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1019-1030
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reinforcement-Based Learning for Process Classification Task
    Autorzy:
    Bashir, Lubna Zaghlul
    Powiązania:
    https://bibliotekanauki.pl/articles/1192874.pdf
    Data publikacji:
    2016
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Reinforcement Learning
    Reward
    Classification
    Bucket Brigade Algorithm
    Opis:
    In this work, we present a reinforcement-based learning algorithm that includes the automatic classification of both sensors and actions. The classification process is prior to any application of reinforcement learning. If categories are not at the adequate abstraction level, the problem could be not learnable. The classification process is usually done by the programmer and is not considered as part of the learning process. However, in complex tasks, environments, or agents, this manual process could become extremely difficult. To solve this inconvenience, we propose to include the classification into the learning process. We apply an algorithm to automatically learn to achieve a task through reinforcement learning that works without needing a previous classification process. The system is called Fish or Ship (FOS) assigned the task of inducing classification rules for classification task described in terms of 6 attributes. The task is to categorize an object that has one or more of the following features: Sail, Solid, Big, Swim, Eye, Fins into one of the following: fish, or ship. First results of the application of this algorithm are shown Reinforcement learning techniques were used to implement classification task with interesting properties such as provides guidance to the system and shortening the number of cycles required to learn.
    Źródło:
    World Scientific News; 2016, 36; 12-26
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reinforcement Learning in Ship Handling
    Autorzy:
    Łącki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/117361.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Ship Handling
    Reinforcement Learning
    Machine Learning Techniques
    Manoeuvring
    Restricted Waters
    Markov Decision Process (MDP)
    Artificial Neural Network (ANN)
    multi-agent environment
    Opis:
    This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2008, 2, 2; 157-160
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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