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Simulation-Based Development of Heating Control System
Tomeček, Jan ; Rozman, Jaroslav (referee) ; Janoušek, Vladimír (advisor)
This thesis is about optimalization of boiler heating from external sources. I have created a simulation model of Heating Control System. Subsequently, using a simulation model, I proposed possible optimizations for water heating control. The used optimization method was deep Q-learning. The result of this work shows the use of simulation for the development and optimalization of control systems.
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Reinforcement Learning for Starcraft Game Playing
Chábek, Lukáš ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This work focuses on methods of machine learning for playing real-time strategy games. The thesis applies mainly methods of Q-learning based on reinforcement learning. The practical part of this work is implementing an agent for playing Starcraft II. Mine solution is based on 4 simple networks, that are colaborating together. Each of the network also teaches itself how to process all given actions optimally. Analysis of the system is based on experiments and statistics from played games.
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Design of autonomous vehicle simulator
Machač, Petr ; Králík, Jan (referee) ; Věchet, Stanislav (advisor)
Tato práce se zabývá simulačními prostředky pro vývoj algoritmů pro řízení autonomních automobilů. V zásadě lze rozdělit na dvě části, na rešeršní, teoretickou, a praktickou, vývojovou. V té prvně zmíněné je uveden přehled dostupných nástrojů pro simulaci autonomních vozidel, jedná se jak o nástroje open-sourcové tak placené. Dále se v teoretické části popisuje princip a nástroje, resp. enginy pro řešení dynamických rovnic na počítači. Důraz je kladen na fyzikální engine Box2D který je dle zadání této práce využit ve druhé části teze pro vývoj vlastního prostředí simulujícího autonomní automobil.
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Intelligent Reactive Agent for the Game Ms.Pacman
Bložoňová, Barbora ; Zbořil, František (referee) ; Drahanský, Martin (advisor)
This thesis focuses on artificial intelligence for difficult decision problemes such as the game with uncertainty Ms. Pacman. The aim of this work is to design and implement intelligent reactive agent using a method from the field of reinforcement learning, demonstrate it on visual demo Ms.Pacman and compare its intelligence with well-known informed methods of playing games (Minimax, AlfaBeta Pruning, Expectimax). The thesis is primarily structured into two parts. The theoretical part deals with adversarial search (in games), reactivity of agent and possibilities of machine learning, all in the context of Ms. Pacman. The second part addresses the design of agent's versions behaviour implementation and finally its comparison to other methods of adversarial search problem, evaluation of results and a few ideas for future improvements.
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Strategic Game with Uncertainity
Tulušák, Adrián ; Šimek, Václav (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous functional system for the game Scotland Yard by using artificial intelligence methods for game theory and machine learning. The problem is solved by algorithm of game theory - Alpha Beta. There was an attempt to use machine learning, but it proved to be unsuccessful due to the large number of states for expansion and insufficient computational recourses. The solution using Alpha Beta algorithm was tested on human players and it proved the ability of artificial intelligence to fully compete against real players. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. Based on these experiments, the thesis also introduces some improvements that could utilize machine learning and extend the existing solution.
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Artificial Intelligence for the Santorini Board Game
Rybanský, Adam ; Kocour, Martin (referee) ; Beneš, Karel (advisor)
The aim of this thesis was to use create an intelligent agent using Reinforcement learning to play Santorini, a 2-player zero-sum board game. The specific algorithm that was implemented was a modified version of Deep Q-learning, with the use of convolutional neural networks (one for training and the other for estimating future Q-value) and a memory of previously executed moves, from which the agent chooses randomly during training. Numerous experiments resulted in 2 final models. One was trained by playing against basic bots, with gradually increasing difficulty. The other was trained by playing against itself from the start. The outcome shows that the model playing against itself produces better results, however both models still perform worse than a bot which uses heuristic function.
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Deep Learning Methods for Machine Playing the Scotland Yard Board Game
Hrkľová, Zuzana ; Janoušek, Vladimír (referee) ; Zbořil, František (advisor)
Táto práca sa zaoberá metódami hlbokého učenia, ktoré sú aplikovateľné na stolné hry s neurčitosťou. V rámci práce boli naštudované princípy učenia s posilňovaním, s hlavným zameraním na Q-learning algoritmy, spomedzi ktorých bol vybraný Deep Q-Network algoritmus. Ten bol následne implementovaný na zjednodušených pravidlách stolnej hry Scotland Yard. Konečná implementácia bola porovnaná s metódami Alpha-Beta a Monte Carlo Tree Search. S výsledkov vyplinulo, že schovávaný hráč riadený DQN algoritmom predstavoval pre ostatné metódy najťažšieho protihráča, narozdiel od hľadajúcich hráčov, ktorým sa nepodarilo zlepšiť existujúce riešenia. Napriek tomu, že implementovaná metóda nedosiahla lepšie výsledky oproti doposiaľ existujúcim metódam, ukázalo sa, že potrebuje najmenej výpočetných zdrojov a času na vykonanie daného ťahu. To ju robí najperspektívnejšou zo spomínaných metód na budúcu možnú implementáciu originálnej verzie danej hry.
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Simulation-Based Development of Heating Control System
Tomeček, Jan ; Rozman, Jaroslav (referee) ; Janoušek, Vladimír (advisor)
This thesis is about optimalization of boiler heating from external sources. I have created a simulation model of Heating Control System. Subsequently, using a simulation model, I proposed possible optimizations for water heating control. The used optimization method was deep Q-learning. The result of this work shows the use of simulation for the development and optimalization of control systems.
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