What is Reinforcement Learning?

Introduction to Reinforcement Learning

Reinforcement learning comes under machine learning. It is defined as the act of making the machines perform better according to the environment. Usually, machines use the trial and error method to learn new things. If a robot is introduced into a new environment, it acts according to the user. 

So, there are some positive rewards and negative comments. Based on the negativity, it changes its way of performing better. Reinforcement Learning means strengthening or supporting the activity with the required elements or materials.

The agent must find the best possible solution from the above diagram to reach the destination. The reinforcement method is a sub-division of machine learning. Usually, we humans design machines by the artificial intelligence method. 

These AI machines decide how to react to the environment and agents. If it gets more likely feedback, it is satisfied or changes its way of thinking or action to avoid the unlikely feedback.

Division of Reinforcement

The machines designed using artificial Intelligence react with the environment based on the following types.

Reinforcement divisions are explained below

o   Positive Reinforcement

o   Negative Reinforcement

o   Extinction Reinforcement

o   Punishment Reinforcement

Positive Reinforcement

As the name indicates positive, it tends to act positively, which satisfies the user. The machines are trained so that they can provide the best solution to the user at their convenience. 

Hence it results in more development in artificial intelligence technology. If the action gets rewarded or gets a likely response, it is recorded and repeated. The machine stores the positive response and thinks it is the best possible for the environment. 

The environment gets easily attracted to positive actions and encourages more. Hence machines store their day-to-day activity and repeat the best solutions to the user.

A real-time example considering the above event will be, if we buy soap online, we get various suggestions, including perfume, body lotion, deodorant, etc. If we receive a notification like this, we desire to buy any related products, thus increasing sales. 

Hence artificial Intelligence automatically stores which item should suggest to the user according to their purchase.

Negative Reinforcement

If the actions performed by the agent receive negative and unsatisfied comments or feedback, then it is called negative Reinforcement. 

Stop the negatively affecting process in the environment to increase the positivity between the agent and the environment. 

The above diagram displays a mother who asks her child to complete the work and won't let her play until she finishes her work. 

The action taken by the mother is to decrease the negative Reinforcement and enhances the positive Reinforcement. The same logic is applied to the concept of a machine. It won't repeat the actions which are not satisfied. 

It won't suggest a product to the customers who are not more likely to buy.

Extinction Reinforcement

Extinction Reinforcement occurs if the process gets bored or doesn’t receive any rewards from the environment. The agent changes or stops the repeated actions. The process of decreasing the existing behavior is called Extinction Reinforcement.

Extinction reinforcement is stopping or not responding to actions that are responded to earlier. For example, if the dog owner trains the dog to clap its hand, he will reward it. As days pass, the action becomes normal, and not asking the dog to clap its hand as it is less rewarding. Hence it applies to machines. 

The user gets bored of getting the same product suggestion every time; therefore, if responses or reward decreases, the machine stops the same suggestion, or that action won't repeat.

Punishment Re-inforcemen

As the name indicates the action performed by the agent which receives negative feedback or not likely by the environment. Based on the rewards between the environment and the agent, the punishment occurs. 

Punishment reinforcement takes place not to repeat the unrewarding actions again and again. It is divided into two types' positive punishment and negative punishment. Both positive and negative punishment takes place according to the environment.

Key factors of Reinforcement

The key factors of reinforcement  includes, Agent(), Environment(), Action(), State(), Reward(), Policy(), Value(), QValue().Rest of the factors are affecting the environment and agent.

Pros of Reinforcement Learning

o   Reinforcement is the process of self-reacting where the agent has to perform regarding the environment.

o   The agent changes its process and action  based on the reward and feedback.

o   It plays a major role in artificial Intelligence.

Summary

From the tutorial, we have discovered the importance of Reinforcement Machine Learning. Reinforcement is a technology where machines do human work according to the environment. It reduces the workforce and gives the desired result as much as possible.