Performance Analysis of Reinforcement Learning Algorithms and Models Based on the 3D-Pinball Game
Kyeongsoo Kim, Young-Sun Yun · Journal of Software Assessment and Valuation · 2023
Game research applying reinforcement learning has demonstrated that AI can outperform humans.In this study, we extend this approach to '3D-Pinball', a game similar to 'Video Pinball' but with greater complexity.Three reinforcement learning algorithms -DQN, Double DQN, and Dueling Double DQN -are applied to both CNN-based models using visual data and MLP-based models extracting appropriate features from high-dimensional data.These are then evaluated in '3D-Pinball'.Restrictions on gameplay time and lives were implemented for efficient experimentation.Results showed that the Dueling Double DQN algorithm applied to the MLP-based model yielded the highest performance increase and game scores, underscoring the superior performance of simpler models in restricted environments.Consequently, it appears that model development considering constraints is required for superior performance in learning and game environments.