Thesis Details
Hluboké učení AI v herních prostředích
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.
Deep Learning, Machine learning, AI, Reinforcement Learning, Imitation Learning, GAIL, Q-Learning, Proximal Policy Optimalization, ML-agents, Unity, Game environments, Agents, rewards and penalties, Genetic Algorithms, Neuroevolution, Neuroevolution of Augmented Topologies, Evolution Strategies, Markov Decision Process, Neural network
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Kekely Lukáš, Ing., Ph.D. (DCSY FIT BUT), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Veselý Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
@bachelorsthesis{FITBT24101, author = "Kristi\'{a}n Gl\'{o}s", type = "Bachelor's thesis", title = "Hlubok\'{e} u\v{c}en\'{i} AI v hern\'{i}ch prost\v{r}ed\'{i}ch", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "slovak", url = "https://www.fit.vut.cz/study/thesis/24101/" }