Evaluation of DQN and Double DQN Algorithms in Flappy Bird Environment

DOI

10.2991/978-94-6463-370-2_24How to use a DOI?

Keywords

Reinforcement Learning; Deep Learning; Flappy Bird

Abstract

To solve problems involving sequential decision-making, Deep Reinforcement Learning (DRL) combines the advantages of Reinforcement Learning (RL) and Deep Learning (DL). These fusions are skilled at training agents for video games because they use neural networks to approximate nonlinear functions. The Deep Q Network (DQN), a key algorithm in this field, tends to overestimate Q-values despite its effectiveness. The Double Deep Q Network (Double DQN) was presented to address this. The Kera’s framework in PyCharm is used in this study to examine the practical application and comparative analysis of DQN and Double DQN in the Flappy Bird game. To speed up its training, improvements were also made to the DQN model. The enhanced DQN performed better than the traditional DQN but less well than the Double DQN, according to the results. This study delves into the practical application and comparative analysis of DQN and Double DQN in the Flappy Bird game, utilizing the Kera’s framework in PyCharm. Additionally, enhancements were made to the DQN model to expedite its training. Results affirmed that the enhanced DQN outperformed the conventional DQN but lagged the Double DQN. The training loss trajectory further substantiated Double DQN’s superiority in mitigating overestimation issues.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhenyu Chen
PY  - 2024
DA  - 2024/02/14
TI  - Evaluation of DQN and Double DQN Algorithms in Flappy Bird Environment
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 214
EP  - 220
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_24
DO  - 10.2991/978-94-6463-370-2_24
ID  - Chen2024
ER  -