Explorative analysis of the effects of hyperparameters on Q-learning in the test of Algorithmic IQ - Dlouhý, Matyáš; Vadinský, Ondřej; Máša, Petr; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
This thesis explores how Q-learning hyperparameter configurations affect Q-learning performance in the Algorithmic Intelligence Quotient (AIQ) test. Using a dataset of 945 configurations across 100,000 interactions and 10,000 environments within the test, four analytical approaches are applied: regression models, decision trees, time series analysis and association rule mining. The findings show that learning and exploration rate influence short- and long-term performance, with higher values enabling fast initial improvement but lower values leading to stronger later AIQ scores. Upper extreme values of discount factor and eligibility trace decay rates were found to severely hinder performance, especially when combined with high learning rates. The analyses also reveal compensatory relationships between hyperparameters, namely the learning rates, eligibility trace decay rates and discount rates. Extreme upper values of gamma or lambda are not detrimental to performance on their own, but they do result in poor performance when not compensated by a moderate value of the other parameter. Higher learning rates similarly demand smaller eligibility trace decay rates. Finally, a Python script implementing the full suite of analyses was developed, allowing the same exploratory procedure to be applied to new AIQ test data with other agents, opening an avenue for future refinement and research.
Vedoucí práce: Ondřej Vadinský
Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2026
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2026
Způsob přístupu: Internet
Keywords: aplikovaná informatika [obor bakal. práce]; bakalářské práce; bachelor's theses