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An Extended Bandit Learning Game Approach

Citation Author(s):
Jun Dai
Submitted by:
Jun Dai
Last updated:
DOI:
10.21227/m9d4-3d74
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Abstract

The extended bandit learning game algorithm can search the best solution for the hybrid discrete-continuous strategy space. At each learning time, the player can quickly decide based on a finite discrete strategy pool, thereby improving the learning efficiency. With the development of the learning time, the dynamic strategy pool can efficiently evolve to extend the whole hybrid discrete-continuous space, thereby avoiding missing the real best solution the hybrid discrete-continuous space.  Therefore, the proposed extended bandit learning game algorithm can achieve the quick search for hybrid discrete-continuous strategy spaces, and offers high applicability for the hybrid discrete-continuous resource optimization problem in unknown dynamic scenarios.

Instructions:

Matlab 2019.b. For details, please contact us by e-mail:djd2021@163.com.

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