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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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Heuristics for the Scotland Yard Board Game
Cejpek, Michal ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis explores the possibility of using deep and reinforcement learning algorithms to solve problems with incomplete information. The main algorithm under investigation is PPO – Proximal Policy Optimization. In order to test the suitability of the PPO algorithm, a simplified implementation of the Scotland Yard game was created as well as an environment for training and testing the algorithms. From performed experiments, it emerged that the PPO algorithm is very suitable for solving problems with incomplete information. The agents very quickly gained a sense of the game’s goals and built appropriate strategies to meet those goals through training.
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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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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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