Looking for a good approach for building an automated director for a racing game spectator mode

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I’m building a tool that should assist a director to broadcast a racing game. I want this tool to suggest the human director which car to focus on and with which camera (among the available ones). I can access quite a lot of data about the current race so I can extrapolate some parameters(like car positions, how many cars near to each other there are, how close they are, last time the camera was switched etc) to be used in the decision making process. I would like the AI to learn from the human director in order to suggest him according to his “direction style”.

My idea is to split the problem in 2 sub problems: the first is the choice of the car to focus on and the second is the choice of the camera to use (or cameras, since is fairly common to switch cameras while following the same car). My plan was to use some sort of Q-learning, rewarding the AI whenever one of the generated suggestions is chosen by the director but I guess it would be really difficult to define a set of states and moreover it would probably take ages before it would start to give some useful suggestions.

Are there some other good approaches I could consider? I’m also thinking about using a neural network so maybe the learning process would be faster.

PS: I know this could be solved algorithmically but as I said I would like something that can learn the “direction style” of the user and moreover I’m not a director my self so finding the right balance between the weights of all the variables would be hard and difficult to test.

submitted by /u/GVannu
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