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Guided Reinforcement Learning for Motor Skills
Karabelly, Jozef ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
Cieľom tejto práce je prezentovať prehľad aktuálneho výskumu v oblasti posilovaného učenia pohybu s predlohou a identifikovať potenciálne smery výskumu. Okrem toho práca predstavuje vylepšenú metódu učenia fyzikálne simulovaných animácií postáv založenú na aktuálnych metódach. Predtrénovaný model ukazuje potenciál lepších výsledkov na rôznych nových úlohách. Vlastný dataset bol nazbieraný pre účely pretrénovania modelu predstaveného v tejto práci. Na základe výsledkov z vykonaných experimentov sú odprezentované možné budúce vylepšenia a smery výskumu.
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Deep Learning AI in Game Environments
Glós, Kristián ; Bobák, Petr (referee) ; Polášek, Tomáš (advisor)
This thesis is focused on analysing deep learning algorithms and their ability to complete given tasks implemented in game environments created via the Unity game engine. Secondary objective was to research and specify possible use-cases of deep learning during game development. The algorithms used fall into Reinforcement learning, Imitation learning and Neuroevolution, while Reinforcement learning was used throughout the whole game scene development cycle. Analysis and results were collected through training the networks in different game scene states and other factors.
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Training Intelligent Agents in Unity Game Engine
Vaculík, Jan ; Chlubna, Tomáš (referee) ; Matýšek, Michal (advisor)
The goal of this work is to design applications, which demonstrate the power of machine learning in video games. To achieve this goal, this work uses the ML-Agents toolkit, which allows the creation of intelligent agents in the Unity Game Engine. Furthermore, a series of experiments showing the properties and flexibility of intelligent agents in several real-time scenarios is presented. To train the agents, the toolkit uses reinforcement learning and imitation learning algorithms.
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Training Intelligent Agents in Unity Game Engine
Vaculík, Jan ; Chlubna, Tomáš (referee) ; Matýšek, Michal (advisor)
The goal of this work is to design applications, which demonstrate the power of machine learning in video games. To achieve this goal, this work uses the ML-Agents toolkit, which allows the creation of intelligent agents in the Unity Game Engine. Furthermore, a series of experiments showing the properties and flexibility of intelligent agents in several real-time scenarios is presented. To train the agents, the toolkit uses reinforcement learning and imitation learning algorithms.
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Deep Learning AI in Game Environments
Glós, Kristián ; Bobák, Petr (referee) ; Polášek, Tomáš (advisor)
This thesis is focused on analysing deep learning algorithms and their ability to complete given tasks implemented in game environments created via the Unity game engine. Secondary objective was to research and specify possible use-cases of deep learning during game development. The algorithms used fall into Reinforcement learning, Imitation learning and Neuroevolution, while Reinforcement learning was used throughout the whole game scene development cycle. Analysis and results were collected through training the networks in different game scene states and other factors.
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