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# Neural net for game state prediction

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I want to use neural network to predict next state in very simple problem(see picture)/

So the input would be pos x, pos y, dir x, dir y, and output is the same as input.

What is the best way to do this in terms of training data?

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1. This sounds a lot like a homework problem (largely because it has absolutely no practical use). We don't help people with homework here. Against the rules.
2. Use math instead (largely because using a NN here has no practical use). If your teacher gets snotty saying that wasn't the assignment, tell said teacher, "I decided that it would be preferable to use the right tool for the job." See if you get extra credit.

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I want to add that you will have to do 2. also if you really would want to set up your training data..

What is the best way to do this in terms of training data?

Well, tell it when it is wrong. When is this the case?

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Your NN 'output' actually would be the direction the object will move for its next state  (8 point compass value) from which the  xy vector and then xy position would be derived.

Understanding that the environment can be generalized to the same directional surrounding 8 grid tiles either being a vertical/horizontal wall to be the terrain sensor inputs (probably 8X2=16 neurons for that because of handling the corner interaction added complexity)

As typical for most NN solutions, the translation of the raw inputs into generalized inputs applicable to NN processing is the key.

From that you now have probably already been taught how many layers/neuron count and even type of NN to use for this problem.

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