As we said in Chapter 1, Overview of Keras Reinforcement Learning, the goal of RL is to learn a policy that, for each state s in which the system is located, indicates to the agent an action to maximize the total reinforcement received during the entire action sequence. To do this, a value function estimation is required, which represents how good a state is for an agent. It is equal to the total reward expected for an agent from the status s. The value function depends on the policy with which the agent selects the actions to be performed.
Monte Carlo methods for estimating the value function and discovering excellent policies do not require the presence of a model of the environment. They are able to learn through the use of the agent's experience alone or from samples of state sequences, actions, and rewards obtained from interactions between agent...
