Hands-On Reinforcement Learning for Games

The elements of RL

Every RL agent is comprised of four main elements. These are policy, reward function, value function, and, optionally, model. Let's now explore what each of these terms means in more detail:

• The policy: A policy represents the decision and planning process of the agent. The policy is what decides the actions the agent will take during a step.
• The reward function: The reward function determines what amount of reward an agent receives after completing a series of actions or an action. Generally, a reward is given to an agent externally but, as we will see, there are internal reward systems as well.
• The value function: A value function determines the value of a state over the long term. Determining the value of a state is ...