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This thesis investigates the complexity of reinforcement learning (RL) in variable video game environments, specifically using the 2D platformer Geometry Dash as a case study. It explores how different game parameters and random disturbances affect the learning processes of RL algorithms, employing a deep Q-learning approach with a convolutional neural network. The research aims to enhance understanding of RL complexity and provide a foundation for future studies in this area.
- Author
- matiasfrizza451
- Language
- EN