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.