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Posilované učení pro hru typu Bomberman
Adamčiak, Jakub ; Beran, Vítězslav (oponent) ; Hradiš, Michal (vedoucí práce)
Cieľom tejto bakalárksej práce je návrh, implementácia a trénovanie modelov posilňovaného učenia na hru typu Bomberman. Je postavená na prostredí Bomberland od firmy CoderOne. Toto prostredie bolo vyvinuté za účelom vzdelávania a výskumu v odvetí umelej inteligencie. V tejto práci rozoberám rôzne nastavenia a problémy s implementovaním agenta do prostredia. Vyskúšal som 2 politiky (MLP a CNN), 2 algoritmy (PPO a A2C) a 5 druhov neurónových sietí pre extrakciu vlastností za pomoci knižníc stable baselines 3 a pytorch. Celkový čas trénovania týchto modelov bol dokopy 1207 reálnych hodín, 4168 strojových hodín a 271 miliónov herných krokov. Aj keď bolo trénovanie neúspešné, táto práca ukazuje proces implementácie modelu posilňovaného učenia do prostredia Gym.
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Combined heat and power production planning in a waste-to-energy plant using machine learning
Kollmann, Marek ; Miklas, Václav (oponent) ; Touš, Michal (vedoucí práce)
This research deployed machine learning to optimize day-ahead production planning in Waste-to-Energy (WtE) plants, grappling with issues like noisy data, uncontrollable external consumption, and fluctuating steam production due to waste as a fuel source. The primary aim was to accurately predict the power transferred to the grid, which was achieved by creating a comprehensive model consisting of seven sub-models in cascade. Each sub-model was critically evaluated using standard metrics like R2 and Mean Relative Error. Findings revealed a significant improvement in prediction accuracy, resulting in more balanced production plans and reduced operational penalties. The approach led to an estimated annual increase of power delivered by 13% and profit by 2.6 million CZK for a specific plant.
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Anomaly Detection by IDS Systems
Gawron, Johann Adam ; Homoliak, Ivan (oponent) ; Očenášek, Pavel (vedoucí práce)
The goal of this thesis is to familiarize myself, and the reader, with the issues surrounding anomaly detection in network traffic using artificial inteligence. To propose and subsequently implement a methodology for creating an anomaly classifier for network communication profiles. The classification method should be able to efficiently and accurately identify anomalies in network traffic to avoid generating false outputs. During the research of the issue, IDS systems, various types of attacks, and approaches to anomaly detection and classification were examined. In evaluating the effectiveness, several standard methods were examined and used to express the quality of classifiers.
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SVM Algorithm Training for DDoS on SDN Networks
Murtadha ; Shujairiand ; Škorpil, Vladislav
Despite the flexibility provided by SDN technology is also vulnerable to attacks such as DDoS attacks, Network DDoS attack is a serious threat to the Internet today because internet traffic is increasing day by day, it is difficult to distinguish between legitimate and malicious traffic. To alleviate the DDoS attack in the campus network, to mitigate this attack, propose in this paper to classify benign traffic from DDoS attack traffic by SVM of the classification algorithms based on machine learning. As the contribution of this paper is to train the SVM algorithmwhich has been used in the approach for the training process. Due to the complexity of the dataset, using a type of kernel called a polynomial kernel to accomplish non-linearity discriminative. The results showed that the traffic classification was with the highest accuracy 96 %.
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Posilované učení pro hru typu Bomberman
Adamčiak, Jakub ; Beran, Vítězslav (oponent) ; Hradiš, Michal (vedoucí práce)
Cieľom tejto bakalárksej práce je návrh, implementácia a trénovanie modelov posilňovaného učenia na hru typu Bomberman. Je postavená na prostredí Bomberland od firmy CoderOne. Toto prostredie bolo vyvinuté za účelom vzdelávania a výskumu v odvetí umelej inteligencie. V tejto práci rozoberám rôzne nastavenia a problémy s implementovaním agenta do prostredia. Vyskúšal som 2 politiky (MLP a CNN), 2 algoritmy (PPO a A2C) a 5 druhov neurónových sietí pre extrakciu vlastností za pomoci knižníc stable baselines 3 a pytorch. Celkový čas trénovania týchto modelov bol dokopy 1207 reálnych hodín, 4168 strojových hodín a 271 miliónov herných krokov. Aj keď bolo trénovanie neúspešné, táto práca ukazuje proces implementácie modelu posilňovaného učenia do prostredia Gym.
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