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Aplikace posilovaného učení v řízení Smart Home
Biel, Gabriel ; Zbořil, František (referee) ; Janoušek, Vladimír (advisor)
Táto práca skúma, ako môže strojové učenie zlepšiť riadenie inteligentných domácností s dôrazom na optimalizáciu riadenia teploty a zvýšenie energetickej účinnosti. Konkrétne sa porovnávajú dva pokročilé algoritmy posilňovaného učenia, Deep Q-Learning (DQL) a Proximal Policy Optimization (PPO). Tieto modely sú testované v simulovanom prostredí, ktoré napodobňuje reálne podmienky, aby sa zhodnotila ich schopnosť prispôsobiť sa správaniam užívateľov a zmenám v prostredí. Ukázalo sa, že model PPO je obzvlášť účinný vďaka svojej stabilite a schopnosti predpovedať návrat obyvateľov. Tento výskum ponúka cenné poznatky o praktických aplikáciách AI technológií v inteligentných domácnostiach.
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Using Reinforcement learning and inductive synthesis for designing robust controllers in POMDPs
Hudák, David ; Holík, Lukáš (referee) ; Češka, Milan (advisor)
Jednou ze současných výzev při sekvenční rozhodováním je práce s neurčitostí, která je způsobena nepřesnými senzory či neúplnou informací o prostředích, ve kterých bychom chtěli dělat rozhodnutí. Tato neurčitost je formálně popsána takzvanými částečně pozorovatelnými Markovskými rozhodovacími procesy (POMDP), které oproti Markovským rozhodovacím procesům (MDP) nahrazují informaci o konkrétním stavu nepřesným pozorováním. Pro rozhodování v takových prostředích je nutno nějakým způsobem odhadovat současný stav a obecně tvorba optimálních politik v takových prostředích není rozhodnutelná. K vyrovnání se s touto výzvou existují dva zcela odlišné přístupy, kdy lze k problému přistupovat úplnými formálními metodami, a to buď s pomocí výpočtu beliefů či syntézou konečně stavových kontrolérů, nebo metodami založenými na nepřesné aproximaci současného stavu, reprezentované především hlubokým zpětnovazebným učením. Zatímco formální přístupy jsou schopné dělat verifikovatelná a robustní rozhodnutí pro malá prostředí, tak zpětnovazebné učení je schopné škálovat na reálné problémy. Tato práce se pak soustředí na spojení těchto dvou odlišných přístupů, kdy navrhuje různé metody jak pro interpretaci výsledku, tak pro vzájemné předávání nápověd. Experimenty v této práci ukazují, že z této symbiózy mohou těžit oba přístupy, ale také že zvolený přístup ke trénování agentů už sám o sobě řádově překonává současné systémy pro trénování agentů na podobných úlohách.
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Reinforcement Learning for Bomberman Type Game
Adamčiak, Jakub ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This bachelor's thesis aims to develop, implement and train reinforcement learning models for a Bomberman-type game. It is based on Bomberland environment from CoderOne. This environment was created for education and research in the field of artificial intelligence. In this thesis I tackle the settings and problems of implementing agent into the environment. I used 2 policies (MLP and CNN), 2 algorithms (PPO and A2C) and 5 setups of neural networks for feature extraction with the use of libraries stable baselines 3 and pytorch. Total training time resulted in 1207 real-world hours, 4168 computing hours and 271 milions of time steps. Although the training was not successful, this thesis shows the process of implementing a reinforcement learning model into a Gym environment.
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Application of Reinforcement Learning in Autonomous Driving
Vosol, David ; Zbořil, František (referee) ; Janoušek, Vladimír (advisor)
This thesis is focused on the topic of reinforcement learning applied to a task of autonomous vehicle driving. First, the necessary fundamental theory is presented, including the state-of-the-art actor-critic methods. From them the Proximal policy optimization algorithm is chosen for the application to the mentioned task. For the same purpose, the racing simulator TORCS is used. Our goal is to learn a reinforcement learning agent in a simulated environment with the focus on a future real-world application to an RC scaled model car. To achieve this, we simulate the conditions of remote learning and control in the cloud. For that, simulation of network packet loss, noisy sensory and actuator data is done. We also experiment with the least number of vehicle's sensors required for the agent to successfully learn the task. Experiments regarding the vehicle's camera output are also carried out. Different system architectures are proposed, among others also with the aim to minimize hardware requirements. Finally, we explore the generalization properties of a learned agent in an unknown environment.
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Changes of enzyme activities in fruits during long-term storage
Ferdová, Jitka ; Melounová,, Jitka (referee) ; Márová, Ivana (advisor)
This study is focused on study of changes of enzyme and low-molecular weight antioxidants in different fruits during long-term storage. In theoretical part individual low-molecular weight antioxidants and enzymes are described. The main causes of fruit decay and some possibilities of fruit preservation and storage are summarized. As biological material some common fruits were chosen - green and red apples, peaches, plums and white grapes. The fruits were stored in laboratory, cellar, in refrigerator and in freezer. In freezing experiments some ways of fruit preparation and processing were tested and their influence on fruit antioxidant status was compared. Shortened storage experiment was applied on blueberries, cranberries, raspberries and strawberries too. In fruits some group parameters – total antioxidant status, dry mass content, ascorbate level, total flavonoids and total phenolics were analyzed spectrophotometrically. Individual flavonoids and phenolics were determined by RP-HPLC/UV-VIS and on-line LC/PDA/ESI-MS. Antioxidant enzyme activities (superoxide dismutase SOD, catalase CAT, polyphenol oxidase PPO and lipoxygenase LOX) were measured by spectrophotometry. The surface microscopy and cultivation of moulds from fruit surface were performed too. Influence of storage conditions on biological activities is dependent on fruit sort. Freezing is the most suitable procedure for long-term storage without significant changes of active substance content. Long-term storage in controlled temperature conditions and/or atmosphere is usable for fruits with longer storage period. In these fruits stabile levels of antioxidant enzymes are stored for relatively long time. Some of enzymes act synergistically. Enzyme activities differed according to storage phase; at the beginning mainly high SOD and LOX activities were observed. CAT and PPO are probably activated as defence systems in rippened and/or damaged fruits. Levels of total as well as individual low molecular weight antioxidants varied during storage in all sorts, generally, increased course with longer storage period can be observed.
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Influence of storage conditions on content of biologically active substances in apple fruits.
Ferdová, Jitka ; Čarnecká, Martina (referee) ; Márová, Ivana (advisor)
This study deals with antioxidants in diet and their effects on human organism. Further, it summarizes the agents affect the quality of apples in the course of long-term storage and it outlines the possibility of defence against them. In the experimental part methods of determination of antioxidant enzymes superoxid dismutase (SOD), catalase and polyphenol oxidace (PPO) in apples were introduced. The enzymes were measured in apples tissues in liquid nitrogen after 158 days in normal or modified atmosphere. In Apple the quantitative and qualitative analysis of proteins was realized. Further, some low molecular antioxidants (total phenolics, total flavonoids and vitamin C) as well as total antioxidant status were measured in frozen raw juice. This values were compared with values from apples analyzed immediately after the harvesting. Artificial inoculation with fungi was made in the last part and the fruitfulness was observed.
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Modifications to the river direction taking into account protection and revitalization
Hala, Matěj ; Kotaška, Stanislav (referee) ; Duchan, David (advisor)
The content of this bachelor thesis includes a hydraulic assessment of the river Dřevnice in the section of km 3,380 - 6,820. The thesis describes in detail the character of the flow and the assessment of the existing condition of the channel using the HEC-RAS 6.3.1 software. Then a design of nature-based flood protection measures for the Q100 flow is carried out. Finally, the hydraulic assesment of the proposed PPO is described. The work also includes map outputs of individual water spreading, depths, velocities and sample cross-sections of the proposed flood protection measures.
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Application of Reinforcement Learning in Autonomous Driving
Vosol, David ; Zbořil, František (referee) ; Janoušek, Vladimír (advisor)
This thesis is focused on the topic of reinforcement learning applied to a task of autonomous vehicle driving. First, the necessary fundamental theory is presented, including the state-of-the-art actor-critic methods. From them the Proximal policy optimization algorithm is chosen for the application to the mentioned task. For the same purpose, the racing simulator TORCS is used. Our goal is to learn a reinforcement learning agent in a simulated environment with the focus on a future real-world application to an RC scaled model car. To achieve this, we simulate the conditions of remote learning and control in the cloud. For that, simulation of network packet loss, noisy sensory and actuator data is done. We also experiment with the least number of vehicle's sensors required for the agent to successfully learn the task. Experiments regarding the vehicle's camera output are also carried out. Different system architectures are proposed, among others also with the aim to minimize hardware requirements. Finally, we explore the generalization properties of a learned agent in an unknown environment.
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Reinforcement Learning for Bomberman Type Game
Adamčiak, Jakub ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This bachelor's thesis aims to develop, implement and train reinforcement learning models for a Bomberman-type game. It is based on Bomberland environment from CoderOne. This environment was created for education and research in the field of artificial intelligence. In this thesis I tackle the settings and problems of implementing agent into the environment. I used 2 policies (MLP and CNN), 2 algorithms (PPO and A2C) and 5 setups of neural networks for feature extraction with the use of libraries stable baselines 3 and pytorch. Total training time resulted in 1207 real-world hours, 4168 computing hours and 271 milions of time steps. Although the training was not successful, this thesis shows the process of implementing a reinforcement learning model into a Gym environment.
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Changes of enzyme activities in fruits during long-term storage
Ferdová, Jitka ; Melounová,, Jitka (referee) ; Márová, Ivana (advisor)
This study is focused on study of changes of enzyme and low-molecular weight antioxidants in different fruits during long-term storage. In theoretical part individual low-molecular weight antioxidants and enzymes are described. The main causes of fruit decay and some possibilities of fruit preservation and storage are summarized. As biological material some common fruits were chosen - green and red apples, peaches, plums and white grapes. The fruits were stored in laboratory, cellar, in refrigerator and in freezer. In freezing experiments some ways of fruit preparation and processing were tested and their influence on fruit antioxidant status was compared. Shortened storage experiment was applied on blueberries, cranberries, raspberries and strawberries too. In fruits some group parameters – total antioxidant status, dry mass content, ascorbate level, total flavonoids and total phenolics were analyzed spectrophotometrically. Individual flavonoids and phenolics were determined by RP-HPLC/UV-VIS and on-line LC/PDA/ESI-MS. Antioxidant enzyme activities (superoxide dismutase SOD, catalase CAT, polyphenol oxidase PPO and lipoxygenase LOX) were measured by spectrophotometry. The surface microscopy and cultivation of moulds from fruit surface were performed too. Influence of storage conditions on biological activities is dependent on fruit sort. Freezing is the most suitable procedure for long-term storage without significant changes of active substance content. Long-term storage in controlled temperature conditions and/or atmosphere is usable for fruits with longer storage period. In these fruits stabile levels of antioxidant enzymes are stored for relatively long time. Some of enzymes act synergistically. Enzyme activities differed according to storage phase; at the beginning mainly high SOD and LOX activities were observed. CAT and PPO are probably activated as defence systems in rippened and/or damaged fruits. Levels of total as well as individual low molecular weight antioxidants varied during storage in all sorts, generally, increased course with longer storage period can be observed.
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