After an extensive debugging session, the project is now complete (this time for real).

The main problem was the model was exploring too much. Even though it knew what to do, it was just refusing to use its knowledge so I made the epsilon decay faster so it switches to exploitation earlier.

After this I had to find the sweet spot number of episodes to ensure consistency in its trials. This was found to be 3300 after observations based on trial and error.

Unfortunately, since DQN is not very stable, there will be a difference in quality whenever you run the program. Some landings may be smooth, others may be…questionable.

I had a lot of fun with this project and I encourage anyone that wants to get into reinforcement learning to check it out.

There is no “interactive demo” since its a simulation. So check out the code on github and I will attack this video to the project page as well.