This blog covers the following:
What is Reinforcement Learning?
Reinforcement learning is a Machine Learning field that allows you to take suitable actions in specific situations to maximize rewards. This technology trains machines to learn several models and make important decisions. Further, the main concern of this method is how software agents take certain actions and make decisions in the given environment.
Watch this comprehensive YouTube video on Reinforcement Learning crafted especially for beginners:
In the case of reinforcement learning, the solution is not given prior. However, reinforcement agents simply decide what needs to be done to perform a given task. Since there is no dataset available for training, this method allows machines to learn from their own experience. Further, the software agents in reinforcement learning are rewarded for their positive behavior, thereby encouraging these actions; whereas, for any negative behavior, they are punished. Their ultimate goal is to learn the required behavior by maximizing their overall reward.
Now that you have briefly understood what reinforcement learning is, let’s take a look at some of its applications in various domains and understand its use.
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Reinforcement Learning Applications
After getting detailed knowledge of reinforcement learning, let’s discuss some of its applications in domains such as gaming, healthcare, robotics, digital marketing, and more.
Applications of Reinforcement Learning in Games
Gaming applications and reinforcement learning run hand-in-hand in most cases. Gaming applications are a challenging domain that helps you test various reinforcement learning algorithms.
Several gaming applications make use of reinforcement learning, one of them being the game of Othello. In this, reinforcement learning, along with a combination of the function approximation technique, allows one to avoid issues of modeling and dimensionally. In Othello, the agents of reinforcement learning learn to play the game with no knowledge or techniques given by humans. They learn to play the game through several hit-and-trial methods by trying out all possible paths and, in the end, selecting one with the least number of hurdles. This way, the agents find the best suitable way to win.
The use of RL in the popular game of Othello must have helped you understand this technology a little better. However, for understanding reinforcement learning more, let’s check out its use in the field of finance.
Reinforcement Learning Applications in Finance
Reinforcement learning has helped develop several innovative applications in the financial industry. This combined with Machine Learning has made several differences in the domain over the years. Today, there are numerous technologies involved in finance, such as search engines, chatbots, etc.
Several reinforcement learning techniques can help generate more return on investment, reduce cost, improve customer experience, etc. Reinforcement learning and Machine Learning, together, can result in improved execution while approving loans, measuring risk factors, and managing investments.
One of the most popular applications of reinforcement learning in finance is portfolio management. It is building a platform that allows you to make significantly more accurate predictions with regards to stock and other such investments, thereby providing better results. This is one of the main reasons why most investors in the industry wish to create these applications to evaluate the financial market in a detailed manner. Moreover, many of these portfolio management applications, including Robo-advisors, allow you to generate more accurate results with time.
Reinforcement learning has made its way through several industries, two of which have already been mentioned above—the gaming and the finance industries. However, it plays a major role in the healthcare industry as well. So, next, you will come across some of the top reinforcement learning medical applications.
Reinforcement Learning Applications in Healthcare
With technology improving and advancing on a regular basis, it has taken over almost every industry today, especially the healthcare sector. With the implementation of reinforcement learning, the healthcare system has generated better outcomes consistently. One of the most common areas of reinforcement learning in the healthcare domain is Quotient Health.
Quotient Health is a software app built to target reduced expenses on electronic medical record assistance. The app achieves this by standardizing and enhancing the methods that create such systems. The main goal of this is to make improvements in the healthcare system, specifically by lowering unnecessary costs.
Another among the popular examples of reinforcement learning in healthcare is KenSci. The KenSci application makes use of this technology to predict various diseases and treatments that further assist both patients and practitioners to diagnose the ailments at earlier stages. Also, it allows you to make predictions regarding several health threats that might hit the population via advancement in disease, pinpointing patterns, and building precarious markers.
This is just a gist of the advancement made in the healthcare industry with the help of reinforcement learning. Now, let’s dive into the category of reinforcement learning examples in the field of image processing.
Reinforcement Learning Applications in Image Processing
The image processing field is a subcategory of the healthcare domain. It is, somewhat, a part of the medical industry but having a domain of its own. Honestly, reinforcement learning revolutionized not only image processing but the medical industry at large. However, here, we will discuss some of the applications of this technology in image processing alone.
PathAI is a reinforcement learning and Machine Learning integrated tool that helps pathologists make a precise diagnosis at a faster rate. Besides, it assists practitioners to establish the patients that may benefit from a new form of therapy or treatment.
Quantitative insights is another example in this category. A computerized breast MRI device, Quantx uses this technology, aiming to improve the precision and swiftness of identifying breast cancer. The main objective of this technology is to enhance results for the diagnosis of patients.
These are just a few examples of RL in the image processing domain. Now that we have covered the applications of this technology in the medical industry, let’s read about some of its applications in other industries, such as marketing, robotics, business, and more.
Reinforcement Learning Applications in Marketing
Reinforcement learning is a nascent technology that is applied in numerous spheres, allowing you to maximize outputs and escalate numerous processes. In the domain of digital marketing, reinforcement learning promises to revamp and also modernize numerous operations.
A commonly used application of this technology in digital marketing is the development of personalized recommendation systems. In the case of a customized product recommender, companies can offer a personal touch to customers with respect to their purchase decisions. However, marketers often face obstacles such as insufficient customer data, popularity biases, etc. while they deliver individualized recommendations to their customers.
In this scenario, reinforcement learning proves its capability to solve several issues of dynamic digital marketing such that marketers can deliver high-quality recommendations, resonating with the behavior, needs, and specific preferences of the customers.
After learning about the applications of reinforcement learning in the field of digital marketing, let’s get into the domain of robotics.
Reinforcement Learning Applications in Robotics
The ultimate aim of reinforcement learning is to endow robots and machines with several abilities, including the abilities to learn, adapt to, and improve in tasks with constraints changing dynamically based on autonomous learning and exploration. Here, let’s discuss one of the most popular reinforcement learning real-world applications, namely, energy minimization in bipedal walking.
In this application of reinforcement learning, this technology is used while there are several distinct policy representations that are evaluated and proposed. In turn, these policies provide viable solutions to face various challenges, such as adaptability, correlations, globality, convergence, etc.
This blog on ‘Applications of Reinforcement Learning’ has covered the meaning of reinforcement learning with the help of its applications. You have come across numerous real-world applications of reinforcement learning technology in several fields, including healthcare, gaming, and finance, among many others.
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