3.9 Optimality and Approximation 3.10 Summary Let us summarize the elements of the reinforcement learning problem that we have presented in this chapter.
Reinforcement learning is about learning from interaction how to behave in order to achieve a goal.
The reinforcement learning agent and its environment interact over a sequence of discrete time steps. A policy is a stochastic rule by which the agent selects actions as a function of states. The agent's objective is to maximize the amount of reward it receives over time. The return is the function of future rewards that the agent seeks to maximize. It has several different definitions depending upon whether one is interested in total reward or discounted reward .