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The goal is to create an AI to play a simple game, tracking a horizontally moving dot across the screen which increases speed until no longer tracked.

I would like to create an AI to behave similarly to a real test subject. I have a large amount of trials that were recorded of many months, position of dot on screen and user cursor position over time.

I would like to train the network on these trials so that the network behaves similarly to a real test subject and I can then obtain very large amounts of test data to observe how changing the parameters of the game affects the networks ability to track the moving dot.

I am interested in learning about the underlying code of neural networks and would love some advice on where to start with this project. I understand AIs can get very good at performing different tasks such as snake, or other simple games, but my goal would be to have the AI perform similarly to a real test subject.

fredmaggiowski
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Michael1997
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1 Answers1

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Your question is a bit broad, but i'll try to answer nonetheless.

To imitate a subjects behavior you could use and LSTM network which has an understanding of the state it's in (in your case the state may include information about how fast and in which direction the dot is going and where the pointer is) and then decides on an action. You will need to feed your data (the dot coordination and users behavior) into the the network.

A simpler yet effective approach would be using simple MLP network. Your problem does not seem like a hard one and a simple network should be able to learn what a user would in a certain situation. However, based on what you mean by "perform similarly to a real test subject" you might need a more complex architecture.

Finally there are GAN networks, which are somewhat complicated if you're not familiar with NNs, are hard and time-consuming to train and in some cases might fail to train at all. The bright side is they are exactly designed to imitate a probability distribution (or to put it more simply, a set of data).

There are two more important notes to mention:

  1. The performance of your network depends heavily on your data and the game. for example, if in your dataset users have acted very differently to the same situation MLP or LSTMs will not be able to learn all those reactin.
  2. Your network can only imitate what it's taught. So if you're planning to figure out what a human agent would do under some conditions that never happened in your dataset (e.g. if in your dataset the dot only moves in a line but you want it to move in a circle when experimenting) you won't get good results.

hope this helps.

Bahman Rouhani
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