Adaptive Q-learning algorithm with constant learning rate parameters (Contributo in atti di convegno)

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  • Adaptive Q-learning algorithm with constant learning rate parameters (Contributo in atti di convegno) (literal)
Anno
  • 2001-01-01T00:00:00+01:00 (literal)
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  • D'Orazio T. , Cicirelli G. , Distante A. (2001)
    Adaptive Q-learning algorithm with constant learning rate parameters
    in Intelligent Systems and Control, Tampa, Florida, 19-22 Novembre 2001
    (literal)
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  • D'Orazio T. , Cicirelli G. , Distante A. (literal)
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  • The product is publisehd in the Proc. of ISC 2001 which is organized by the IASTED society. (literal)
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  • In robotic tasks adaptive learning systems are required as soon as a change of behavior is necessary to manage new situations. In this paper we compare the convergence rate of Q-values to the optimal ones considering different learning-rate parameters (constant and decreasing ones). In particular we suggest a weighted updating of the Q-values that allows both the adaptivity to new situation and the smoothing of noise. The used experimental test-bed is a grid-world domain where a simulated agent learns to reach a particular goal state. After some iterations the goal position in the environment is moved and the algorithm is able to learn a new policy to accomplish the task. (literal)
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Titolo
  • Adaptive Q-learning algorithm with constant learning rate parameters (literal)
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