# Training a reinforcement learning model with multiple images

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 (e.g., 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.