CODEWITHSUNDEEP
machine-learningneural-networkartificial-intelligencebackpropagation
Smileynator
Smileynator

Reputation: 697

How to compute an error for neural networks with unknown ideal?

Ok, so i set up a neural network through some trial and error. Going into backpropagation next. But in order to do that, i need to calculate my error on the outputs.

The situation i made for my testing area is the following. I have a car, in the corner of a maze, to navigate to the other corner of said maze. The inputs are:

Outputs are:

All inputs and outputs are obviously normalized between -1 and +1. The car can only move forward so -1 = 0 speed, +1 = max speed. (i know the speed is kind of useless, but i wondered if it could figure out that max speed is going to get it there faster, or if it would automatically slow for a corner when it otherwise wouldn't be able to turn quick enough)

So all i know is that i want the car from current position, to end position of the maze. Not having to worry about anything else myself.

Back to the error calculation part of this script. All guides i read and followed, are asking for an error compared to output. As in, output - ideal = error The problem is that in order for it to progress to the exit of the maze, i have no clue what the ideal way of steering or speed is at that point.

To my view this can mean one of two things: 1. There are other ways to approach the error which i can use but have no knowledge of. Or there is a way to figure out the error somehow mathematically. 2. I am expecting way to much from the neural network with way to little input/output. Or asking it something it could never do. Thus i can't ever calculate the proper error.

Anyone that can help me figure out this error would be appreciated! Otherwise please explain what i am doing wrong or how i can improve the task or I/O.

Thanks in advance!

Upvotes: 0

Views: 76

Answers (1)

lejlot
lejlot

Reputation: 66805

You are confusing two things. You read about supervised training of neural networks. While you try to solve the problem which is not supervised, it is rather reinforcment learning. In such setting you do not have access to "ideal" answer. You can only more or less say that current state is good/bad (like achieveing the exit of the maze is a good place, while being far is bad). Just either focus on supervised learning problems, and then resources you use are ok, or stick to your problem of a maze and switch to reinforcment learning.

Upvotes: 3

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