to build such functionwe will start with specific set of algorithms in reinforcement learning called -learning algorithms to illustrate theseconsider the initial state of gamewhich we'll call state your spaceship and the aliens are all in their starting positions thenassume we have access to magical " -tablewhich tells us how much reward each action will earnstate action reward state shoot state right state left shoot action will maximize your rewardas it results in the reward with the highest value as you can seea -table provides straightforward way to make decisionsbased on the observed statepolicystate -look at -tablepick action with greatest reward howevermost games have too many states to list in table in such casesthe -learning agent learns -function instead of -table we use this -function similarly to how we used the -table previously rewriting the table entries as functions gives us the followingq(state shoot (state right