Enhancing Flappy Bird Performance With Q-Learning and DQN Strategies

Abstract

 Flappy Bird, a classic single-player game, boasts a deceptively simple premise yet proves to be a formidable challenge in achieving high scores. Various algorithms have been employed to improve its performance, yet a comprehensive assessment of Q-Learning and Deep Q-Network (DQN) in the context of this game remains elusive. This study undertakes the task of training Flappy Bird using both Q-Learning and DQN methodologies, showcasing the potency of reinforcement learning within the realm of gaming. Through meticulous comparisons and analyses, the paper uncovers the inherent strengths and weaknesses embedded within these algorithms. This exploration not only fosters a nuanced grasp of Q-Learning and DQN but does so by leveraging a simplistic gaming environment as the proving ground. Strikingly, the experimental results unveil an initial disadvantage for DQN during training, followed by a rapid surge in performance surpassing Q-Learning in mid-training. Conversely, Q-Learning demonstrates an aptitude for swiftly reaching its performance zenith. Both algorithms tout distinct merits: Q-Learning's adeptness in simpler tasks and DQN's reliability in tackling complex states. In conclusion, this study not only discerns algorithmic prowess but lays a foundational framework for broader application across diverse gaming scenarios. By delving into the nuances of Q-Learning and DQN, the paper establishes a clearer path for harnessing the advantages in shaping the future landscape of game optimization.