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    Wyświetlanie 1-3 z 3
    Tytuł:
    Anapplication of decision rules in reinforcement learning
    Autorzy:
    Michalski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/206534.pdf
    Data publikacji:
    2000
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    decision rules
    Q-learning
    reinforcement learning
    rough set theory
    Opis:
    In this paper an application of decision rules to function representation in reinforcement learning is described. Rules are generated incrementally by method based on rough set theory from instances recorded in state-action-Q-value memory. Simulation experiment investigating the performance of the system and results achieved are reported.
    Źródło:
    Control and Cybernetics; 2000, 29, 4; 989-996
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Learning board evaluation function for Othello by hybridizing coevolution with temporal difference learning
    Autorzy:
    Szubert, M.
    Jaśkowski, W.
    Krawiec, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/206175.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    evolutionary computation
    coevolutionary algorithms
    reinforcement learning
    memetic computing
    game strategy learning
    Opis:
    Hybridization of global and local search techniques has already produced promising results in the fields of optimization and machine learning. It is commonly presumed that approaches employing this idea, like memetic algorithms combining evolutionary algorithms and local search, benefit from complementarity of constituent methods and maintain the right balance between exploration and exploitation of the search space. While such extensions of evolutionary algorithms have been intensively studied, hybrids of local search with coevolutionary algorithms have not received much attention. In this paper we attempt to fill this gap by presenting Coevolutionary Temporal Difference Learning (CTDL) that works by interlacing global search provided by competitive coevolution and local search by means of temporal difference learning. We verify CTDL by applying it to the board game of Othello, where it learns board evaluation functions represented by a linear architecture of weighted piece counter. The results of a computational experiment show CTDL superiority compared to coevolutionary algorithm and temporal difference learning alone, both in terms of performance of elaborated strategies and computational cost. To further exploit CTDL potential, we extend it by an archive that keeps track of selected well-performing solutions found so far and uses them to improve search convergence. The overall conclusion is that the fusion of various forms of coevolution with a gradient-based local search can be highly beneficial and deserves further study.
    Źródło:
    Control and Cybernetics; 2011, 40, 3; 805-831
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimal control of dynamic systems using a new adjoining cell mapping method with reinforcement learning
    Autorzy:
    Arribas-Navarro, T.
    Prieto, S.
    Plaza, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/205725.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    optimal control
    cells mapping
    state space
    reinforcement learning
    stability
    nonlinear control
    controllability
    Opis:
    This work aims to improve and simplify the procedure used in the Control Adjoining Cell Mapping with Reinforcement Learning (CACM-RL) technique, for the tuning process of an optimal contro ller during the pre-learning stage (controller design), making easier the transition from a simulation environment to the real world. Common problems, encountered when working with CACM-RL, are the adjustment of the cell size and the long-term evolution error. In this sense, the main goal of the new approach, developed for CACM-RL that is proposed in this work (CACMRL*), is to give a response to both problems for helping engineers in defining of the control solution with accuracy and stability criteria instead of cell sizes. The new approach improves the mathematical analysis techniques and reduces the engineering effort during the design phase. In order to demonstrate the behaviour of CACM-RL*, three examples are described to show its application to real problems. In All the examples, CACM-RL* improves with respect to the considered alternatives. In some cases, CACM- RL* improves the average controllability by up to 100%.
    Źródło:
    Control and Cybernetics; 2015, 44, 3; 369-387
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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