Individual Professional Practice in the Company

V tejto bakalárskej práci popisujem odbornú prax v spoločnosti Tieto Czech s.r.o., priebeh a uplatnenie mojích vedomostí na vykonávaných projektoch. V prvej časti práce popisujem históriu a predstavenie spoločnosti Tieto Czech s.r.o. V druhej časti sa venujem rozboru mojej histórie v Tieto Czech s.r.o., popis môjho pôsobenia vo firme a na projektoch. V tretej časti sa venujem rozboru vývoja projektu a popisu použitých technológií, táto časť je rozdelená na tri časti. V prvej sa venujem fáze plánovania vývoja projektu. V druhej časti sa venujem vývoji back-end a v tretej sa zameriavam na vývoj front-end. V druhej a tretej časti taktiež popisujem použité technológie v danej časti. V poslednej časti sa venujem uplatneným a chýbajúcim znalostiam počas odbornej praxe.In this bachelor thesis I describe my professional practise at the company Tieto Czech s.r.o., its course and applying of knowledge on projects. In the first part I introduce Tieto Czech s.r.o. and describe its history. In the second part I describe my position at Tieto Czech s.r.o. and I also describe projects that I have been working on. In the third part I present development of projects, I describe used technologies, this part is divided into three parts. In the first sub-part I describe the phase of the planning of the development of the project. In the second sub-part I present back-end development and in the third part I describe front-end development. In the second and the third sub-part I also describe technologies used in these parts. In the very last part I am writing about the skills I used and the skills I lacked during my professional practise.460 - Katedra informatikyvýborn

Module Reinforcment Learning for Neuron Net Modeler

Táto diplomová práca sa zameriava na rozšírenie programu Modeler neurónových sieti o modul pre Reinforcement Learning. V tejto práci je opísaný Reinforcement Learning s a bez použitia hlbokého učenia. Taktiež sú v tejto práci opísané problémy, ktoré Reinforcement Learning má a ich možné riešenia a vylepšenia. V ďalšej časti opisujem paralelizáciu pre Reinforcement Learning s neurónovými sieťami. S využitím implementovaných vylepšení boli vykonané experimenty a porovnaná efektivita učenia.This master thesis focuses on extending the program Neural Net Modeler with Reinforcement Learning module. This thesis describes Reinforcement learning with and without deep learning. Furthermore, this thesis deals with problems of Reinforcement learning and their solutions. Further, I describe the parallelization of Reinforcement learning with neural networks. Using implemented solutions, experiments were performed and results compared.460 - Katedra informatikyvýborn

On Region-Free Explicit Model Predictive Control

Abstract We show that explicit MPC solutions admit a closed-form solution which does not require the storage of critical regions. Therefore significant amount of memory can be saved. In fact, not even the construction of such regions is required. Instead, all possible optimal active sets are first extensively enumerated. Then, for each optimal, only the analytical expressions of primal and dual variables are stored. Optimality of a particular if checked by verifying primal and dual feasibility conditions, which are unique for all candidate sets. We show that the required memory storage can be further reduced by only storing the factors for the dual variables. IEEE Conference on Decision and Control (CDC) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-We show that explicit MPC solutions admit a closed-form solution which does not require the storage of critical regions. Therefore significant amount of memory can be saved. In fact, not even the construction of such regions is required. Instead, all possible optimal active sets are first extensively enumerated. Then, for each optimal , only the analytical expressions of primal and dual variables are stored. Optimality of a particular if checked by verifying primal and dual feasibility conditions, which are unique for all candidate sets. We show that the required memory storage can be further reduced by only storing the factors for the dual variables