Elements of Reinforcement Learning

There are 4 common elements of Reinforcement Learning as shown below

1. Policy: Function that basically maps situation to action. It basically states, what action an agent should perform at a given time.
2. Reward Signal: The reward is the feedback given to the agent for a particular action. Reward signal can be called as a numeric value which environment send to the agent as feedback of any action. The reward is given to the agent according to the positive and the negative actions.
3. Value Functions: This is the quantity that quantifies how a particular situation is good for the agent. It can be stated as the future rewards that an agent will get being in a particular state
4. Model of the Environment: This is what defines how an environment will behave when a particular action is performed in a particular state. Also, the agent can predict the next reward and state, according to the current state and action.