Deep Q-Network (DQN) is a method in the field of reinforcement learning that combines Q-learning with deep neural networks. DQN was developed to tackle complex problems where the state space is too large for traditional Q-tables to be feasible.
In DQN, a neural network is used to approximate the Q-function, enabling the selection of optimal actions based on the current state of the environment. This method is notable for its ability to learn from unlabeled data and improve stability during training by storing past experiences (experience replay) and using a separate target network.
Flappy Bird is a popular mobile game that gained immense popularity for its simple yet challenging gameplay. Players control a small, animated bird that must navigate through a series of vertical pipes by tapping the screen to make the bird flap its wings and gain altitude. The objective is to fly as far as possible without colliding with the pipes. The game features retro-style graphics and a catchy soundtrack, contributing to its addictive nature. Despite its straightforward mechanics, Flappy Bird became notorious for its difficulty, leading to high scores and intense competition among players.
If you find this project useful, please give it a star to show your support and help others discover it!To get started with this project, clone the repository using the following command:
git clone https://github.com/TruongNV-hut/AIcandy_DQN_FlappyBird_xcrtkuqo.gitBefore running the scripts, you need to install the required libraries. You can do this using pip:
pip install -r requirements.txtTo train the model, use the following command:
python python aicandy_flappyBird_train_otnlcisl.pyAfter training, you can test the model using:
python python aicandy_flappyBird_test_romddkvc.pyYoutube: https://www.youtube.com/@aicandyvn/videos
To learn more about this project, see here.
To learn more about knowledge and real-world projects on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), visit the website aicandy.vn.
❤️❤️❤️