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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.

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