I tried my hand at some neural nets from scratch but I had some issues. Felt like I was brute forcing results instead of making predictable progress.
With just an input and output layer, I can get to this level of quality super quick. Sometimes it makes it a bit farther but never down to the next loop.
With a middle layer it reaches the same spot but takes a bit longer to train. This is around 50,000 epochs.
Two middle layers doesn't improve the final result, it just takes longer to reach there. This is 150,000 epochs.
I'm guessing I'm having serious problems with overfitting. It easily finds a method that works for the first swerve but it can't modify itself without losing that. Maybe I need use a less naive reinforcement method, or maybe I need to add more starts to the data.
Fun seeing how close it loves riding edges though. The reward function is based off distance travelled not distance along the path.
With just an input and output layer, I can get to this level of quality super quick. Sometimes it makes it a bit farther but never down to the next loop.
With a middle layer it reaches the same spot but takes a bit longer to train. This is around 50,000 epochs.
Two middle layers doesn't improve the final result, it just takes longer to reach there. This is 150,000 epochs.
I'm guessing I'm having serious problems with overfitting. It easily finds a method that works for the first swerve but it can't modify itself without losing that. Maybe I need use a less naive reinforcement method, or maybe I need to add more starts to the data.
Fun seeing how close it loves riding edges though. The reward function is based off distance travelled not distance along the path.
