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Paperspace / DinoRunTutorial

Accompanying code for Paperspace tutorial "Build an AI to play Dino Run"
https://blog.paperspace.com/dino-run
MIT License
323 stars 104 forks source link

Downsample too fast #4

Closed OrNot closed 6 years ago

OrNot commented 6 years ago

The 3 layers of conv with proposed values of stride , pooling .ect. leads the feature map to be small too fast ( supposed 80*80 input). is there any other settings I missed ?

Thanks a lot

ravi72munde commented 6 years ago

Most of the data pixels in the image are empty so small feature map should not hinder the accuracy or speed. I personally believe stripping away some data was necessary as we are processing images real-time as we perform actions. I have in fact got satisfactory results with an image input of mere 20x40 as well. That being said, please try out and let us know if you find a better configuration. The current training configuration requires at least 1 million steps, which is quite OK in case of reinforcement learning but if we can reduce that somehow that's certainly an achievement.

OrNot commented 6 years ago

Thanks for your reply. I am following your codes and try to re-implement with pytorch. I modify the model to align the architecture as proposed in the paper double-dqn. I have run the loops nearly 20000 but the highest score is still under 100. Is it a normal training or should I cease the process to double check my code ?

ravi72munde commented 6 years ago

20000 is a small count to judge. I would suggest keep it for another 20000. If you see no progress, check your code.