The project compared the performance of each of the DQN , DDQN, and Dueling DQN methods in the Cartpole Reinforcement Learning environment.
Cartpole is a basic example of reinforcement learning. The goal is to move the cart and hold it for a long time without dropping the stick that is on the cart. In the Cartpole reinforcement learning environment, agents acquire know-how through trial and error.
The project was trained in Cartpole Reinforcement Learning environment with DQN (Deep-Q-Network), DDQN (Double Deep-Q-Network), and Dueling DQN (Duel Deep-Q-Network) methods, and then the performance was compared.
[DQN]
[DDQN]
[Dueling DQN]
=> Project Description and result (detail) (My Blog)
click Code - Download ZIP and unzip it
- Open the
jupyter notebookwith pytorch installed. - Run
Cartpole_DQN/DDQN/Dueling DQN.ipynb.
If you don't have the jupyter notebook on your computer, install it from this link.
If you don't have the openAI gym library, please install it from this link.
I am looking for someone to help with this project. Please advise and point out.
Please read CONTRIBUTING.md for details on our code
of conduct, and the process for submitting pull requests to us.
- jangThang - Wooyoung Jang - star7sss@naver.com
See also the list of contributors who participated in this project.
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