FLAP-AI BIRD
Abstract & Details
Research Area
Artificial Intelligence and Machine Learning
Keywords
research methodology
engineering innovation
academic publishing
manuscript formatting
journal submission
scientific writing
IJARIIE
paper structure
Abstract
This study presents the application of Deep Q-Learning, a variant of Deep Reinforcement Learning
(DRL), for training an AI agent to play the game Flappy Bird autonomously. Flappy Bird, due to its
dynamic and continuous environment, poses a challenge that tests real-time decision-making capabilities.
The proposed approach utilizes a Deep Q-Network (DQN) which leverages convolutional neural
networks (CNNs) to estimate Q-values directly from pixel input. Core components such as experience
replay, target networks, and epsilon-greedy policies are integrated to ensure stable and efficient learning.
Extensive experimentation highlights how varying hyperparameters such as learning rate, replay buffer
size, and exploration rates impact convergence and gameplay efficiency. The final agent demonstrates
robust decision-making, achieving high scores consistently, and provides insights into DRL's applicability
in constrained, real-time environments.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Ramya BN | Jyothy Institute Of Technology |
| 2 | Chandana C Gowda | Jyothy Institute Of Technology |
| 3 | Shubhashree | Jyothy Institute Of Technology |
| 4 | Yashwanth L | Jyothy Institute Of Technology |
| 5 | Poojitha K.B | Jyothy Institute Of Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
BN, Ramya, Gowda, Chandana C, Shubhashree, L, Yashwanth, & K.B, Poojitha (2025). FLAP-AI BIRD. International Journal of Advance Research and Innovative Ideas In Education, 11(3), 1133-1139.
MLA Style
BN, Ramya, et al. "FLAP-AI BIRD." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, 2025, pp. 1133-1139.
IEEE Style
Ramya BN, Chandana C Gowda, Shubhashree, Yashwanth L, and Poojitha K.B, "FLAP-AI BIRD," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, pp. 1133-1139, 2025.
Vancouver Style
BN Ramya, Gowda Chandana C, Shubhashree, L Yashwanth, K.B Poojitha. FLAP-AI BIRD. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(3):1133-1139.
Harvard Style
BN, Ramya, Gowda, Chandana C, Shubhashree, L, Yashwanth, & K.B, Poojitha (2025) 'FLAP-AI BIRD', International Journal of Advance Research and Innovative Ideas In Education, 11(3), pp. 1133-1139.
Chicago Style
BN, Ramya, et al. "FLAP-AI BIRD." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 1133-1139.
Turabian Style
BN, Ramya, et al. "FLAP-AI BIRD." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 1133-1139.
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