![vector td large game vector td large game](https://img.craftpix.net/2018/04/Tower-Defense-2D-Game-Kit27-768x512.jpg)
You can probably easily figure out what to use for other activation functions. So, for tic-tac-toe, any of your solutions involving 9 inputs should work just fine.Īlso, it helps if you keep the inputs between 0 and 1 if you are using log sigmoid, and -1 and 1 if you are using hyperbolic tangent for your activation function. When you use extra weights, it will take longer to train the network because you need to tune even more values for an optimal network. However, you usually also want to keep the number of weights to a minimum.
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When you are working with neural networks, as long as the data is there, the neural network is usually able to learn how to process it into a useful result. 20 for an opponents Queen and +20 for your own queen? Or would you need something more complex where you define 10+ values for each square, one for each unit-type and player combination? Would it be as simple as using higher input values for more valuable pieces? I.e. Then, when branching into games where the specific pieces' abilities come into play, like in chess, how would you represent this?
![vector td large game vector td large game](https://www.gamesloon.com/games/screenshots/origineel/25122.jpg)
The first being 1 for the X player, the second being 1 for the O player, and both being 0 for a blank
![vector td large game vector td large game](https://img.craftpix.net/2018/04/Tower-Defense-2D-Game-Kit1-600x400.jpg)
In the end, only one in every 3 neurons will have a 1, the other two will have a 0.ġ8 input neurons.
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The first of the set of three indicates whether this square is free or not the second indicates whether the square is occupied by your opponent or not. The first 3 being square 1, the next 3 being square 2, etc. A 0 indicates a free-space, -1 the opponent, and 1 yourself.ĩ input neurons, but using different values, such as 0 for the opponent, 0.5 for free, and 1 for yourself?Ĭould you use larger values? Like 0, 1 and 2?Ģ7 input neurons. So, specifically, for tic-tac-toe, which is a better, or what is the correct representation for the board state?ĩ input neurons, one for each square. Even for something as simple as tic-tac-toe.
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However, when I look at other people's code and examples/tutorials, there seems to be a lot of variance in the way they represent the board state. As I understand, this is how TD-Gammon worked. The idea I like is encoding the board state for the Neural Network with a single output neuron, which gives that board state relative strength compared to other board states. To work my way towards this, I thought it prudent to implement a Neural Network for a few different games first. a "units", or game piece's, position will greatly impact its usefulness in that board state. The video game is a turn-based positional sort of game, i.e. I've been reading a lot about TD-Gammon recently as I'm exploring options for AI in a video game I'm making.