reinforcement-learning
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My code is very poor in learning 2048 game using Double DQN
Firstly, thanks for the great collection of code and articles. The articles were very useful in understanding DQN and implementing it.
However, my code is very bad in learning. I am not sure what is wrong with my code. I am using DDQN and passing rewards based on different criteria. Also the state is just a normalized version of the board itself.
My code repo is here https://github.com/codetiger/MachineLearning-2048 Let me know if you can review and help me understanding why my code doesnot learn anything even after 1000 episodes.
Thank you for utilizing our code for your own code. We will review your code and tell you our opinion!
Hi, I added some optimization techniques to my Agent and got better results.
The agent was trained for 100K episodes in 2x2 grid and got 100% optimal move every time. However, I did not have enough patience to train the agent for 4x4 grid. Updated my repo with new results