Using Game Theory and Reinforcement Learning to Predict the Future
Image credit: New York Times
Baseball is a well known, repeated, finite, adversarial, stochastic game that has a massive amount of available data. On the other hand, Reinforcement Learning (RL) models take significant time and resources to train. By fusing Game Theory and RL, we are answering interesting questions such as “given a video of a pitch, can we compute the utility of a pitch given the desired location, resulting location, and setting?”
