A Glimpse at Reinforcement Learning Method
This paper aims to describe the main theoretical details of Reinforcement Learning (RL). RL is the closest to human
learning among all the other learning methods. Just as an infant learns by itself without any teacher supervision, an agent learns
through RL. A rational agent makes inferences between the effects and their consequences. The term state is used to defined
one of the possible positions where an agent is in the environment. A Reward maybe defined as a credit or a penalty for each
and every state. Actions are possible operations decided by an agent. As an example, an agent navigates through a maze.
Keywords - Bellman equation, Machine learning,Markov property,Reinforcement learning.