Aperiodic Sampled-Data Stabilization of Probabilistic Boolean Control Networks: Deep Q-learning Approach with Relaxed Bellman Operator

In this letter, we investigate the sampled-data state feedback control (SDSFC) problem of Boolean control networks (BCNs). Some necessary and sufficient conditions are obtained for the global stabilization of BCNs by SDSFC. Different from conventional state feedback controls, new phenomena observed the study of SDSFC. Based on the controllability matrix, we derive some necessary and sufficient conditions under which the trajectories of BCNs can be stabilized to a fixed point by piecewise constant control (PCC). It is proved that the global stabilization of BCNs under SDSFC is equivalent to that by PCC. Moreover, algorithms are given to construct the sampled-data state feedback controllers. Numerical examples are given to illustrate the efficiency of the obtained results.

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Simulation of the operation of hydro plants in an electricity market using agent based models - introducing a Q Learning approach

2016 13th International Conference on the European Energy Market (EEM) ◽

10.1109/eem.2016.7521334 ◽

2016 ◽

Cited By ~ 3

Author(s):

Jose Carlos Sousa ◽

Joao Tome Saraiva

Keyword(s):

Electricity Market ◽

Learning Approach ◽

Agent Based Models ◽

Agent Based ◽

Q Learning

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A Cooperative Q-Learning Approach for Online Power Allocation in Femtocell Networks

2013 IEEE 78th Vehicular Technology Conference (VTC Fall) ◽

10.1109/vtcfall.2013.6692027 ◽

2013 ◽

Cited By ~ 11

Author(s):

Hussein Saad ◽

Amr Mohamed ◽

Tamer ElBatt

Keyword(s):

Power Allocation ◽

Learning Approach ◽

Femtocell Networks ◽

Q Learning

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Aperiodic Sampled-Data Stabilization of Probabilistic Boolean Control Networks: Deep Q-learning Approach with Relaxed Bellman Operator

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Simulation of the operation of hydro plants in an electricity market using agent based models - introducing a Q Learning approach

A Cooperative Q-Learning Approach for Online Power Allocation in Femtocell Networks

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