Real World Applications of Reinforcement Learning

Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this blog I will explain in detail the amalgamation of AI in Reinforcement Learning. To begin with, let's understand what Reinforcement Learning is. 

 

 

What is Reinforcement Learning?

 

Reinforcement Learning is a form of AI-based learning approach for a computer system or agent. The agent learns via a sequence of rewards or penalties it receives after completing a task in this style of learning. 

 

The primary goal of this sort of agent is to maximize profits. This agent's capability allows it to be a utility-based agent since it selects the best choice from among the available possibilities to ensure that the user is entirely happy.

 

Reinforcement Learning is the process of gaining knowledge via experience, in which agents are rewarded for good performance and punished for poor performance. This payment is made in the form of a numerical value. 

 

Agents can be either a physical robot or a virtual code that performs a task. As a result, the agent learns what actions it should perform in the future depending on the input.

 

With the assistance of an example, let us clarify this further. Assume an agent is created to clean houses. The agent may do a variety of activities, including mopping, dusting, washing kitchenware, and washing clothing. 

 

Every job in the agent's memory is associated with a particular reward point. Let's say we only want the agent to function for a certain length of time due to a power outage or another issue. 

 

It is discovered that all of the aforementioned duties cannot be done in that time frame. As a result, the agent will perform the jobs with the greatest reward points first. It goes without saying that the more difficult and time-consuming jobs are rewarded with more points. 

 

Thus, the agent completes these jobs first, and the other tasks are simple ones that the user may complete without much effort if he does not want the agent to continue working. As a result, the agent can be used effectively, and our resources are not squandered and can be put to good use.

 

In Reinforcement Learning, on the other hand, the agent must keep account of all previous actions, their influence on the environment, the reward points earned for completing those acts, and the feedback provided for those actions. 

 

The agent increases its performance and usefulness in the future by inferring and learning from these points of previous operations.

 

Also Read | Inverse Reinforcement Learning

 

Components of Reinforcement Learning 

 

The agent (learning agent), the environment (agent interacts with the environment), and the actions are the three main components of reinforcement learning (agents can take actions). By interacting with the environment and gaining incentives for doing actions, an agent learns from it.

 

1. State: The agent's observation of the environment after completing an action. 

 

2. Action: The agent's action on the environment as a result of its observation.

 

3. Reward: The feedback that the agent receives as a result of the activity that it took. It receives a reward if the feedback is favorable, and it receives a penalty if the response is bad.

 

There is an agent as well as a setting. The agent's condition is determined by the surroundings. The agent chooses an action and is rewarded by the surroundings, as well as the new state. This process of learning continues until the objective is reached or another condition is fulfilled.

 

 

Elements of Reinforcement Learning

 

Reinforcement Learning has four elements, which are listed below:

 

1. Policy

 

A policy is a set of rules that govern how an agent acts at any particular time. It connects environmental perceptions to behaviors made in response to those perceptions. A policy is the most important part of the RL since it is the only thing that can define the agent's behavior. 

 

It might be a simple function or a lookup table in certain circumstances, but in others, it could be a search procedure including comprehensive calculation. It might be a deterministic or random policy

 

2. Reward Signal

 

The reward signal determines the purpose of reinforcement learning. The environment gives an instantaneous signal to the learning agent at each state, which is referred to as a reward signal. These bonuses are paid out based on the agent's excellent and negative conduct. 

 

The agent's principal goal is to maximize the overall number of excellent acts rewarded. The reward signal can affect the policy; for example, if an activity chosen by the agent results in a poor reward, the policy may change in the future to choose different actions.

 

3. Value Function

 

The value function tells you how beneficial the scenario and action are, as well as how much money an agent will get. A reward defines the excellent state and action for the future, whereas a value function specifies the current signal for each good and negative action. 

 

Because there can be no value without a reward, the value function is dependent on it. The purpose of estimating values is to increase the number of incentives received.

 

4. Model

 

The model, which mimics the behavior of the environment, is the last component of reinforcement learning. One may draw predictions about how the environment will behave using the model. If a state and an action are supplied, for example, a model can forecast the next state and reward.

 

The model is used for planning, which means it allows you to choose a course of action by examining all possible future scenarios before you encounter them. The model-based approach refers to ways for addressing RL issues with the use of a model. A model-free method, on the other hand, is one that does not use a model.

 

 

Applications of Reinforcement Learning in AI 

 

Are you interested in learning more about the applications that promote positive and negative RL and produce outcomes that will undoubtedly alter the dynamics of many sectors of the economy, consequently instilling value-producing digital innovation in our lives?? 

 

Let's have a look at some of the real-world Reinforcement Learning applications that have successfully transformed the dynamics of industries such as healthcare, marketing, robotics, and many more.

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Applications of Reinforcement Learning in AI

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1. Reinforcement Learning in Marketing

 

Marketing is all about promoting and then selling your brand's or someone else's products or services. Finding the correct demographic that delivers higher returns on investment for you or your organization is a problem in and of itself when it comes to marketing.

 

It's also one of the reasons businesses are spending money on digitally managing various marketing efforts. Your and other organizations, small or large, may expect: 

 

• More display ad impressions in real-time, thanks to real-time bidding that supports the essential features of RL.
• A higher return on investment (ROI) and profit margins
• Anticipating client preferences, emotions, and behavior in relation to your products/services.

 

 

2. Reporting for a Living in Broadcast Journalism

 

Attracting likes and views, as well as tracking the reader's behavior, is considerably easier using different forms of Reinforcement Learning. 

 

Furthermore, since journalists may now be equipped with an RL-based system that keeps an eye on intuitive news content as well as headlines, it may be possible to propose news that meets the continuously changing tastes of readers and other internet users. Some of the benefits Reinforcement Learning provides to readers all around the world include : 

 

• Users' input may now be received instantly by news producers.
• Users are becoming more outspoken, resulting in more dialogue.
• There is no room for misinformation or hostility.

 

 

3. Reinforcement Learning in the Medical Field

 

Healthcare is an important part of our lives, and through DTRs (a sequence-based use-case of RL), doctors can discover the treatment type, appropriate drug doses, and timings for taking such doses. DTRs have the following features: –

 

• A set of standards that confirms a patient's present health state.
• Then they recommend the best therapies for conditions such as diabetes, HIV, cancer, and mental disorders.
• Through their multi-objective healthcare optimization solutions, these DTRs (i.e. Dynamic Treatment Regimes) can lessen or eliminate the delayed impact of therapies if necessary.

 

Also Read | AI in Cancer Detection and Treatment

 

 

4. Robotics using Reinforcement Learning

 

Robotics, without a doubt, makes it easier to teach a robot so that it can accomplish jobs in the same manner that a person can. However, the robotics industry has a greater difficulty today: robots are unable to employ common sense when making moral and social judgments. 

 

Deep Reinforcement Learning, a hybrid of Deep Learning and Reinforcement Learning, comes to the rescue to provide robots with a "Learn How To Learn" model. As a result, robots may now: –

 

• Control their judgments by firmly grabbing a variety of apparent items.
• Robots now know what and how to learn from multiple degrees of abstractions of different sorts of information, something even humans struggle to achieve.

 

 

5. The Real World in Gaming

 

Gaming is something that you, me, and a large portion of the population can no longer live without. We may expect greater performance from our favorite adventure, action, or mystery games thanks to game optimization using Reinforcement Learning algorithms.
 

The AlphaGo example may be used to demonstrate this point. This is a computer programme that defeated the best Go (a difficult classical game) player in October 2015 and then went on to become the best Go player in the world. 

 

Alpha Go's strategy for defeating the player was Reinforcement Learning, which grew in strength as the game was repeatedly exposed to new gameplay problems. There are a slew of different games to choose from, including Alpha Go. You may also improve your favorite games by using the right tools. 

 

 

6. Reinforcement Learning in the Manufacturing Industry

 

Manufacturing is all about creating items that meet our most fundamental requirements and desires. Many organizations are turning to Cobot Manufacturers (or Manufacturers of that can do diverse production jobs with a workforce of more than 100 workers) for their own RL solutions for packaging and quality testing. 

 

Without a doubt, their utilization is speeding up the process of producing high-quality items that can eliminate negative consumer feedback. The lower the number of negative feedbacks, the higher the product's performance and, by extension, the sales margin.

 

 

7. Image Processing using Reinforcement Learning

 

Another essential approach of improving the present version of a picture in order to extract usable information from it is image processing. There are also some steps involved, such as:

 

• Scanning machines are used to capture the picture
• Analyzing and altering it are two different things.
• Using the analyses' output picture for representation and explanation purposes.

 

Also Read | Types of Machine Learning

 

Deep Neural Networks (whose framework is Reinforcement Learning) and other machine learning models can be used to simplify this popular image processing approach. You may use Deep Neural Networks to either improve the quality of an image or hide its information. Later, you may apply it to any computer vision problem.

 

We may conclude from the preceding explanation that Reinforcement Learning is one of the most fascinating and helpful aspects of machine learning. In real life, the agent investigates the surroundings without the need for human interaction. It is the most widely used learning algorithm in AI. 

 

However, there are some situations in which it should not be utilized, such as when there is sufficient data to answer the problem and other machine learning techniques may be employed more effectively. The fundamental problem with the RL algorithm is that certain settings, such as delayed feedback, might alter the learning pace.