Imagine a game where it is a black screen apart from a red pixel and a blue pixel.
Given this game to a human, they will first see that pressing the arrow keys will move the red pixel.
The next thing they will try is to move the red pixel onto the blue pixel.
Give this game to an AI and, it will randomly move the red pixel until a million tries later it accidentally moves onto the blue pixel to get a reward.
If the AI had some concept of distance between the red and blue pixel, it might try to minimiseminimize this distance.
Without actually programming in the concept of distance, if we take the pixels of the game can we calculate a number(s), such as "entropy" or suchlike, that would be lower when pixels are far apart than when close together? It should work with other configurations of pixels. Such as a game with three pixels where one is good and one is bad. Just to give the neural network more of a sense of how the screen looks?
Then give the NN a goal, such as try and minimise"try to minimize the entropy of the board as well as try to get rewardsrewards".
Is there anything akin to this in current research?
