Recently, I’ve read tutorial about neural network in a snake game. (you can see it here—https://towardsdatascience.com/today-im-going-to-talk-about-a-small-practical-example-of-using-neural-networks-training-one-to-6b2cbd6efdb3) But, the way it train the neural network it bit funny—in practice, it depends on knowing what the snake should do in every step, and when he didn’t, train him. Not very useful(cause, you know, I could just program him to do that).
The other way, I guess, is to use log of the game, assume that the players doing the optimal steps (lets say, exept of the end of there game😉) and then train the network on this situations.
But, when I haven’t log of the game, is there a way I can use neural networks for a game? And if there is, how?
Heads-up: nowadays, when people talk about neural networks for games, they really mean deep learning combined with reinforcement learning.
Back to your question: When you don’t have a log of games, you typically have some other way of assessing performance, e.g. assigning a “score” to the state of the game, which you can quantify and optimize.
For a specific well-known example, I think this paper on training to play Atari games with deep reinforcement learning goes over a lot of the actual math / implementation details.