We have learned about the policy function in Chapter 1, Introduction to Reinforcement Learning, which maps the states to actions. It is denoted by π. 

The policy function can be represented as  , indicating mapping from states to actions. So, basically, a policy function says what action to perform in each state. Our ultimate goal lies in finding the optimal policy which specifies the correct action to perform in each state, which maximizes the reward.