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Applications of Reinforcement Learning in Real World

Robin kumar 29 November, 2021 | 3 min read

Reinforcement Learning is a sub-field of Machine Learning which itself is a sub-field of Artificial Intelligence. It implies:

Artificial Intelligence -> Machine Learning -> Reinforcement Learning

Are you now not curious to know about the applications practically supporting positive and negative RL and producing results that can for sure change the dynamics of various sectors of the economy thereby imbibing value-producing digital innovation in our lives!! Let’s know a bit about the real-life applications of Reinforcement Learning which have confidently changed the dynamics of sectors like Healthcare, Marketing, Robotics, and many more.

1. RL in Marketing

Marketing is all about promoting and then, selling the products or services either of your brand or someone else’s. In the process of marketing, finding the right audience which yields larger returns on investment you or your company is making is a challenge in itself.

And, it is one of the reasons companies are investing dollars in managing digitally various marketing campaigns. Through real-time bidding supporting well the fundamental capabilities of RL, your and other companies, smaller or larger, can expect: –

more display ad impressions in real-time. increased ROI, profit margins. predicting the choices, reactions, and behaviour of customers towards your products/services

2. RL in Broadcast Journalism

Through different types of Reinforcement Learning, attracting likes and views along with tracking the reader’s behaviour is much simpler. Besides, recommending news that suits the frequently-changing preferences of readers and other online users can possibly be achieved since journalists can now be equipped with an RL-based system that keeps an eye on intuitive news content as well as the headlines. Take a look at other advantages too which Reinforcement Learning is offering to readers all around the world.

News producers are now able to receive the feedback of their users instantaneously. Increased communication, as users are more expressive now. No space for disinformation, hatred.

3.RL in Healthcare

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 doses of drugs, and timings for taking such doses. Curious to know how is this possible!! See, DTRs are equipped with: –

a sequence of rules which confirm the current health status of a patient. Then, they optimally propose treatments that can diagnose diseases like diabetes, HIV, Cancer, and mental illness too. If required, these DTRs (i.e. Dynamic Treatment Regimes) can reduce or remove the delayed impact of treatments through their multi-objective healthcare optimization solutions.

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4.RL in Robotics

Robotics without any doubt facilitates training a robot in such a way that a robot can perform tasks – just like a human being can. But still, there is a bigger challenge the robotics industry is facing today – Robots aren’t able to use common sense while making various moral, social decisions. Here, a combination of Deep Learning and Reinforcement Learning i.e. Deep Reinforcement Learning comes to the rescue to enable the robots with, “Learn How To Learn” model. With this, the robots can now: –

manipulate their decisions by grasping well various objects visible to them. solve complicated tasks which even humans fail to do as robots now know what and how to learn from different levels of abstractions of the types of datasets available to them.

5.RL in Gaming

Gaming is something nowadays without which you, me, or a huge chunk of people can’t live. With games optimization through Reinforcement Learning algorithms, we may expect better performances of our favourite games related to adventure, action, or mystery.

To prove it right, the Alpha Go example can be considered. This is a computer program that defeated the strongest Go (a challenging classical game) Player in October 2015 and itself became the strongest Go player. The trick of Alpha Goes to defeat the player was Reinforcement Learning which kept on developing stronger as the game is constantly exposed to unexpected gaming challenges. Like Alpha Go, there are many other games available. Even you can also optimize your favourite games by applying appropriately prediction models which learn how to win in even complex situations through RL-enabled strategies.

6. RL in Image Processing

Image Processing is another important method of enhancing the current version of an image to extract some useful information from it. And there are some steps associated like:

Capturing the image with machines like scanners. Analyzing and manipulating it. Using the output image obtained after analysis for representation, description-purposes. Here, ML models like Deep Neural Networks (whose framework is Reinforcement Learning) can be leveraged for simplifying this trending image processing method. With Deep Neural Networks, you can either enhance the quality of a specific image or hide the info. of that image. Later, use it for any of your computer vision tasks

7.RL in Manufacturing

Manufacturing is all about producing goods that can satisfy our basic needs and essential wants. Cobot Manufacturers (or Manufacturers of Collaborative Robots that can perform various manufacturing tasks with a workforce of more than 100 people) are helping a lot of businesses with their own RL solutions for packaging and quality testing. Undoubtedly, their use is making the process of manufacturing quality products faster that can say a big no to negative customer feedback. And the lesser negative feedbacks are, the better is the product’s performance and also, sales margin too.

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