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 - [email protected]
See also the list of contributors who participated in this project.
MIT License
Copyright (c) 2022 jangThang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.