# 283 — Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

In order to create their model, all of a player’s actions (based upon the three million actions coded in the SPORTLOGiQ dataset) were encoded as an input to the network (in contrast with some previous approaches which only used actions when a player was in possession of the puck, or limited to actions that directly resulted in a goal). From this, a $Q$ function is “learned” to predict the probability that, given the current event and the ones that led up to it, the current team scores the next goal. (Understandably,previous approaches have used a Markov model to represent this relationship.)