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Stochastic policy in Q-lerning used for control of AMB
Březina, Tomáš ; Krejsa, Jiří ; Věchet, S.
A great intention is lately focused on Reinforcement Learning (RL) methods. The article is focused on improving model free RL method known as Q-learning used on active magnetic bearing model. Stochastic strategy and adaptive integration step increased the speed of learning approximately hundred times. Impossibility of using proposed improvement online is the only drawback, however it might be used for pretraining and further fined online.
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Learning based control system of four-legged robot
Březina, Tomáš ; Houška, P. ; Singule, V.
Possible discretization technique of the continuous state space of four-legged robot using simultaneous compositions of behaviors is described in the contribution. Compositions are generated by the instances of two basic controllers. The aim is to automatically develop the gait policy. Possible composition strategies are implemented through undeterministic state machine. In the machine design stage the number of both states and transitions could be essetially reduced.
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Q-learning used for control of AMB: reduced state definition
Březina, Tomáš ; Krejsa, Jiří
Previous work showed that stochastic strategy improved model free RL method known as Q-learning used on active magnetic bearing (AMB) model. So far the position, velocity and acceleration were used to describe the state of the system. This paper shows simplified version of controller which uses reduced state definition - position and velocity only. Furthermore the controlled initial conditions domain and its development during learning are shown.
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Použití LWR kompenzátoru pro řízení aktivního magnetického ložiska
Březina, Tomáš ; Pulchart, J. ; Krejsa, Jiří
The active magnetic bearing (AMB) control is a complex problem, as it is fast, unstable and nonlinear system. The contribution deals with the simulation verification of AMB control using conventional PSD controller with nonlinear forward compensator. The automated universal approximator based on locally weighted learning (namely Receptive Field Weighted Regression - RFWR method) is used as the compensator.
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Strategic Analysis of an Enterprise
Knapová, Jitka ; Kovář, František (advisor) ; Březina, Tomáš (referee)
Aim of this thesis was to develop a strategic analysis of the selected firm. The whole thesis is divided into two main parts: the theoretical part and the practical. The theoretical part deals with the characteristics of the basic concepts of strategic management, strategy, vision, external and internal analysis and SWOT analysis. The theoretical foundations are applied in the practical part to the selected firm. In conclusion is given advice.
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