Optimality criteria

The optimality criteria are a measure of goodness of fit of the model created over the data. For example, in supervised classification learning algorithms, we have maximum likelihood as the optimality criteria. Thus, on the basis of the problem statement and objective optimality criteria differs. In reinforcement learning, our major goal is to maximize the future rewards. Therefore, we have two different optimality criteria, which are:

• Value function: To quantify a state on the basis of future probable rewards
• Policy: To guide an agent on what action to take in a given state

We will discuss both of them in detail in the coming topics.