Q learning is a value-based way of delivering information to help an agent decide which action to take. Let's look at an example to better understand this method: In a building, there are five rooms that are connected by doors.
Taking opposite actions suggests updating two Q-values at the same time. The agent will update the Q-value for each action and its inverse action, speeding up the learning process. The renowned test-bed grid world problem is reproduced using a revolutionary Q-learning method based on the concept of opposite action.
One of Q-advantages Learning's is that it can compare the expected utility of various actions without the need for a model of the environment. Reinforcement Learning is a method of problem solving in which the agent learns without the assistance of a tutor.
When given a state x, you learn the projected cost via value iteration. When you use q-learning and take action a while in state x, you get the promised discounted cost.