So far, we have defined a learning rule. To get an agent, we also need a decision rule. To this end, we endow the agent with a “utility function” which assigns a number to each possible world. The number represents the desirability of that world according to the agent’s basic preferences. Now, at each time step, the agent selects the action with the highest expected utility.
