用大量图像训练强化学习模型

Question

I am tentatively trying to train a deep reinforcement learning model the maze escaping task, and each time it takes one image as the input (eg, a different "maze").

Suppose I have about 10K different maze images, and the ideal case is that after training N mazes, my model would do a good job to quickly solve the puzzle in the rest 10K - N images.

I am writing to inquire some good idea/empirical evidences on how to select a good N for the training task.

And in general, how should I estimate and enhance the ability of "transfer learning" of my reinforcement model? Make it more generalized?

Any advice or suggestions would be appreciate it very much. Thanks.

Answer 1

Firstly,

I strongly recommend you to use 2D arrays for the maps of the mazes instead of images , it would do your model a huge favor, becuse it's a more feature extracted approach . try using 2D arrays in which walls are demonstrated by ones upon the ground of zeros.

And about finding the optimized N:

Your model architecture is way more important than the share of training data in all of the data or the batch sizes . It's better to make a well designed model and then to find the optimized amount of N by testing different Ns(becuse it is only one variable, the process of optimizing N can be easily done by you yourself).