Reinforcement Learning: What It Is, Types, Applications

In this tutorial, we will learn what is reinforcement learning, types of reinforcement learning, and its applications. By Monika Sharma Last updated : April 16, 2023

What is Reinforcement Learning?

Reinforcement Learning is a type of learning method for a computer system or an agent which works on Artificial Intelligence. In this type of learning, the agent learns from the series of rewards or punishments which it gets on the completion of any task. The main aim of this type of agent is to get the maximum rewards. This functionality of this agent helps it to be a utility-based agent because here, the agent chooses the best among the available options so that the user is satisfied completely.

Example of Reinforcement Learning

Suppose an agent is designed for house cleaning. There are multiple tasks which the agent can do like mopping, dust cleaning, washing utensils, washing clothes. In the agent’s memory, every task is mentioned with different reward points. Suppose we want the agent to work only for some limited amount of time due to the electricity factor or any other factor. It is found that in that period of time, all the mentioned tasks cannot be completed. So, here, the agent will complete those tasks first which will have the highest reward points. It is obvious that the hard and laborious tasks are assigned higher reward points. So, these tasks are automatically completed first by the agent and the leftover tasks are the easy ones which the user can easily do by himself without many efforts if he does not want the agent to work further. So, in this manner, the agent can be used efficiently, and our resources are not wasted and can be used judicially.

However, in Reinforcement Learning, the agent has to keep track of all the actions performed in pasts, their impact on the environment, the reward points secured on performing those actions and the feedback available for those actions. By inferring and learning from these points of the past activities, the agent improves its performance and utility for the future.

Types of Reinforcement Learning

There are two types of reinforcement learning:

1. Positive Reinforcement

When the agent completes any task, if the feedback or the points for the task are in a positive response, then it is termed as the positive reinforcement. This type of reinforcement increases the performance of the agent as the agent now gets a hint that it has to make decisions and perform tasks in this particular manner to earn more rewards in the future also.

2. Negative Reinforcement

Whenever the agent fails to perform any task as required, in that case, the agent is provided with negative reinforcement. This can be thought as of giving punishment to a child for doing mischiefs. The negative reinforcements tell the agent that such type of performance or such type of decisions must be avoided in the future while solving similar types of problems.

Main Points in Reinforcement Learning

The points/factors on which the performance of the agent which learns through Reinforcements depend:

1. Input

The Agent seeks the initial stage as the input from which it has to start. This is an important phase because all the observations and inferences will be drawn starting from this state, and the past state of the agent will not be considered.

2. Output

The output state that the system will reach after solving a certain problem is not fixed as there are multiple ways of solving a problem and the agent can choose different solution whenever it tries to solve the same type of problem.

3. Training/Learning

The training phase or the Learning Phase is when the agent builds its Knowledge Base from the reward or punishment that it gets based on the output it produces. It is a very important phase in Reinforcement Learning because it helps the agent to understand and learn in the same way as humans. This implements the human behavior in agents which is the main target in Artificial Intelligence.

Applications of Reinforcement Learning

The following are the applications of reinforcement learning:

1. Robotics for Industrial Automation

Most of the industries require man force either as laborers, workers or supervisors. Most of these have the same routine to follow each day and they have to work in the same manner in which they have been escorted. The robots designed to do such type of tasks prove to be more beneficial than humans as they do not require rest and they can be used for unlimited working hours. So, despite that, it makes thousands of people jobless, yet businessmen and great industry men chose the robots over humans for earning more profit.

2. Machine Learning

Reinforcement Learning is one of the major topics in Machine Learning and is currently in trend and is a major source of attraction for many researchers and developers.

3. Training Systems and Self-operating Systems

Reinforcement learning is being used to create different self-operating systems like self-driving cars, automated arms, House cleaning agents, etc. Apart from these, many training systems are also designed using it like the test conducting systems, systems which are able to have a human-like communication, systems which are able to receive any call and reply as per the going conversation, etc.

4. Data Processing

In Intelligent agents and expert systems which work in a dynamic and partially observable environment, the reinforcement learning is an effective and widely used way for Data Processing, as the conditions with uncertainty can be easily handled using it.

Apart from this, there are still many uncovered areas where the Reinforcement Learning finds its application and there are also areas where the research and implementation process of the same is being performed.

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