Using Weka for Game Playing

I am doing a project where I have neural networks (or other algorithms) play each other in poker. After each win or loss, I want the neural network (or other algorithm) to update in response to the error of the loss (how this is calculated is unimportant here).

Weka is very nice and I don't want to reinvent the wheel. However, Weka's API seems primarily designed to train from a dataset. Game playing doesn't use a dataset. Rather, the network plays, and then I want it to update itself based on i开发者_如何学Gots loss.

Is it possible to use the Weka API to update a network instead of a dataset but on one instance and do this over and over again? I'm I thinking about this right?

The other idea I also want to implement is use a genetic algorithm to update the weights in a neural network, instead of the backpropogation algorithm. As far as I can tell, there is no way to manually specify the weights of a neural network in Weka. This, of course, is vital if using a genetic algorithm for this purpose.

Please help :) Thank you.

Normally weka learning algorithms are batch learning algoritms. What you need are incremental classifier.

From weka docs

Most classifiers need to see all the data before they can be trained, e.g., J48 or SMO. But there are also schemes that can be trained in an incremental fashion, not just in batch mode. All classifiers implementing the weka.classifiers.UpdateableClassifier interface are able to process data in such a way.

See UpdateableClassifier interface to which classifiers implement it.

Also you may look MOA Massive Online Analysis tool which is closely related with weka and all of its classifiers are incremental due to constraints of online learning.

Weka, as far as I can tell, does not do online learning (which is what you're asking about).

It might be better to investigate using competitive analysis for your game.

You may have to reinvent the wheel here. I don't think it's a bad use of time.

I'm currently implementing a learning classifier system, which is pretty simple. I'd also advise looking into these kinds of algorithms. There is an implementation on the internet, but I still prefer to code my own.