Determination of Q-function optimum grid applied on asynchronous electric motor control task

Determination of Q-function optimum grid applied on asynchronous electric motor control task

Paper in proceedings (conference paper)

cs

Asynchronous electric motor control task can be successfully solved using reinforcement learning based method called Q-learning. The main problem to solve is the convergence speed. Two-phase Q-learning can be used to speed up the learning process. Efficient prelearning phase uses mathematical model and next phase using for tutorage real system. This method can increase learning speed significantly. When the table is used as Q-function approximation, the learning speed and precision of found controllers depend highly on the Q-function table grid properties. The paper is focused on finding the optimal division of grid.

Asynchronous electric motor control task can be successfully solved using reinforcement learning based method called Q-learning. The main problem to solve is the convergence speed. Two-phase Q-learning can be used to speed up the learning process. Efficient prelearning phase uses mathematical model and next phase using for tutorage real system. This method can increase learning speed significantly. When the table is used as Q-function approximation, the learning speed and precision of found controllers depend highly on the Q-function table grid properties. The paper is focused on finding the optimal division of grid.

Asynchronous electric motor, Q-learning, control

Asynchronous electric motor, Q-learning, control

2004-05-10

Institute of Thermomechanics, Academy of Sciences of the Czec Republic

Svratka

80-85918-88-9

Enigneering Mechanics 2004, National Conference with International Participation

177–

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