Top 10 Advantages of Deep Reinforcement Learning

Top 10 Advantages of Deep Reinforcement Learning
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Have you ever watched a video on YouTube only to realize that YouTube recommends more similar videos? If yes, you’re not alone because this blog will address how that happens. We live in an ever-changing world, and this means that technology becomes better as time goes by. Over the last few decades, humanity has made significant steps towards achieving what seemed impossible. Technological tools are everywhere and we use them in almost all aspects of our lives. The rise of robotics, machine learning, natural language processing, artificial intelligence, and many others explains how much the world has developed. This blog aims to explain the meaning of deep reinforcement learning, its advantages, and its applications in the real world. To help you have a clear picture of deep reinforcement learning, let’s start with the basics.

What is Deep Reinforcement Learning?

Deep Reinforcement Learning (DRL) is a branch of artificial intelligence. DRL is a combination of Reinforcement Learning (RL) with Deep Learning (DL). Most times, technology experts have quoted DRL as the most fascinating aspect of artificial intelligence because it enables machines to learn the same way people do. DRL incorporates two systems, which are reward and penalty. This means that if an action produces the desired outcome, it is rewarded or reinforced. If an action produces poor outcomes, it is penalized and eliminated. So, this helps the machines to always remember the good actions because they get reinforced and try to forget the poor actions. This behavior is very key because it enables these machines to make proper decisions using their previous experience. DRL is very popular today because of its ability to solve complex consequential decision-making problems. Because DRL can learn different levels of data abstractions, it can solve many complicated tasks with accuracy and faster. Sometimes, it’s very easy to confuse deep learning and reinforcement learning, although the two terms are different. Deep learning uses the current data to teach machines how to predict the outcome. Deep learning mostly uses the brain’s artificial neural network that mimics the human brain to process vast sums of data. Reinforcement learning, on the other hand, uses trial and error and does not require the current data to predict the outcome. Instead, reinforcement learning learns from mistakes to maximize the rewards and reduce the chances of repeating the past errors. Below are the top 10 advantages of deep reinforcement learning.


As of now, there are over five million games ever created in the world. These games play an important part in human life because they help people relax, focus and even improve memory. All the games you know today have one thing in common, and that is DRL. Most of the games we have in the world today use a certain type of technique that proves to be beyond human abilities. By the time a game is being called “smart”, it must have gone through a series of trial and error for months, even years. Why? Because with this training, the gaming software, with the help of DRL, learns how to keep a memory of what produces the best results. What do I mean by this? When these games are being trained, they make both excellent results and mistakes. For excellent outcomes, this software is reinforced and for any mistake done, the software is penalized. This helps the machines to stay away from these negative outcomes. To help you understand, let me use an example. The human brain learns through trial and error, right? This means that anytime you do a good thing, and you are rewarded for it, you work harder to get more rewards. But what happens when you make a mistake and get shunned? You avoid repeating the same mistake by using the mistake as a lesson. This is how DRL in games works. Many games have used DRL and are some of the most famous games today. A good example is the AlphaGo Zero game, which used the game of Go to learn and create better outcomes. After 40 days of competing against itself, the Alpha Zero game defeated the AlphaGo Master version, making it one of the best games in the world.


We live in the age of robotics. Robotics has been in the making for the last few decades and, as of now, they are being implemented in some areas of work. Robotics are usually designed to perform tasks traditionally carried out by humans but with accuracy and more efficiency. Inventors use DRL to train these robotics to perform various tasks better than humans and at a faster rate. The best thing about robotics is that they can perform hundreds of tasks at once and in a shorter period. An outstanding example where DRL and robotics meet is in the manufacturing industries. First, robotics prototypes are exposed to a large artificial neural network with a reinforcement framework to train them to create desired outcomes. After a while, inventors remove this reinforced software from the prototype and place it in the real robots. With time, these robotics learn how to perform better by doing repetitive tasks without getting tired. This explains why robotics is so common in industries because of their accuracy, performance of hundreds of tasks, ability to work 24/7, and working at faster rates.

Marketing and Advertising

Marketing is one of the major money-making models we have in the 21st century. The most challenging part about marketing and advertising is to get profit for every dollar you spend as a business owner. Also, it becomes very difficult to know which advertisement campaign will bring more investors and huge profits using mere marketing tools. Thanks to DRL, marketing and advertising can be done remotely by software to produce accurate results within a few seconds. A decade ago, companies used to lose lots of money by investing in wrong ideas and running campaigns that consume huge sums of money, only to produce very insignificant results. DRL provides online marketers with a reliable way of minimizing costs and maximizing their return on investment. A group of researchers from Alibaba Group created a Multi-Agent Reinforcement Learning (MARL) tool to help the company in an advertisement of its products. After a while, the MARL gave a company a higher ROI of 240% using the same cost it was using before. How cool is that?

Predicting Customer’s Responses

Customers are a huge part of any business. A business is often defined by the number of customers it serves and how well it treats them. Over the years, customer behaviors have developed as the world exposes them to digitalization. Unlike years ago, nowadays customers are looking for a shop or company that will cater to every aspect of their needs. But what happens when you serve your customers well and you minted to raise the price of the same products? It must be challenging. Deciding to increase the prices is one thing while telling the same narrative to your customers is another separate entity. What brings many businesses down is the inability to help their customers understand why there is this sudden shift in prices. Failure to communicate well with your customers comes from a lack of tools that analyze customer behaviors and shopping patterns. Using DRL tools can help you understand how customers are going to react after raising the prices. For example, if you’re planning to raise the price of dresses by $10 and it does not impress customers, you can stop this for a while because if you continue, customers will not make a purchase. If, for example, DRL tools show that customers are okay with this increase in shoes instead of dresses, then you increase the price of the shoes and you’ll make more profits.

Creation of Personalised Recommendations

Personalized recommendations are another smart tactic technology uses to attract people to certain content and products. This recommendation simplifies your work by saving your time and energy instead of spending your entire time searching for things. To help you get it right, let me use an example. If you go to Amazon today and google something, i.e., “best laptop,” DRL tools will deliver that to you. Upon clicking the laptop, the DRL tool will recommend other different laptops related to the one you’re looking for. This helps you have multiple laptops to choose from in terms of color, space, size, a manufacturing company, etc., and this enables you to make an informed decision. By getting the product you need, the sellers also reap profits from recommending specific high-quality products to you.

Healthcare Applications

Healthcare is one major department that has highly taken advantage of DRL. Over the years, health professionals have learned that DRL tools can save millions of lives when well used. As of today, it is possible to give treatment to patients using policies derived from DRL and RL systems. These tools analyze patients’ information, such as drugs, tests, symptoms, medical history, and other important factors to help health caregivers make proper decisions. One of the most successful DRL uses in the medical field is the creation of dynamic treatment regimes (DTR). DTR is software that is used in the treatment of patients with chronic illnesses such as HIV/AIDS, cancers, hypertension, diabetes, etc. Once DRL is fed with patients’ information, it uses it to come up with personalized care tailored aimed at giving better treatment options, drug dosages, appointment dates, nutritional advice, etc. It is being used also in critical care units (ICU) to help develop care for patients in coma.

Logistics and Transportation

Machine learning and artificial intelligence go hand in hand in the logistics and transportation sectors. A decade ago, one of the major challenges that this department was facing was how to control the movement of goods remotely. Thanks to technology today because it’s possible to access goods being transported in a container remotely. DRL and Machine learning tools help to detect the movement of goods from the dispatch point to their destination. The supply chain professionals, with the help of these tools, can monitor the conditions in which the goods are being transported in. For example, companies that transport perishable goods such as flowers, fruits, and vegetables use DRL sensors to monitor the humidity, temperature, and general condition of these goods on transit. These tools are so advanced in such a way that they can even provide visibility of goods, which ensures maximum protection, especially of expensive goods such as jewelry. DRL sensors also show weather and road conditions, helping companies make better choices.

Cost Reduction

Every company looks forward to creating better ways of reducing the cost of production. Many companies rely on human labor to perform various activities. The problem with this approach is that human labor is limited in terms of the work it delivers per day and also adds more costs to the company. Training people to perform different jobs in any industry is not only super expensive but is also time-consuming and tiresome. DRL algorithms can minimize human error, increase profits and produce goods at a faster rate. Installing the DRL software and training may cost a company immense sums of money, but it’s worth it. Why? Because you’ll only train it once but will work for you for years, performing at a faster rate and giving high levels of accuracy.

Social Media

Social media is one of the many applications that have embraced the use of DRL. Over the years, the number of social media users has increased, paving the way for the adoption of DRL tools. Facebook, for example, has taken advantage of reinforcement tools by creating Horizon. Horizon is an open-source reinforcement learning tool that works to perfect production systems. The DRL tools, with machine learning, improve video quality while streaming and give personalized notifications to Facebook users. This reinforcement makes it possible for Horizon to perform video conferencing integrations, high-quality voice services, file sharing, privacy settings, and many other factors that make Facebook the most popular platform.

Self-Driving Cars

Technology has shifted the automotive industry by storm by introducing self-driving cars. The experts of the industry have shed some light on the possibility of adopting DRL in autonomous driving. When this technology was introduced, many doubted if it’ll work, but Tesla is one company that has proven it is indeed effective. We can apply this DRL in various aspects of self-driving cars, such as speed limits, collision sensors, parking alerts, dynamic pathing, and many others. For example, this technology can learn automatic parking codes or policies, to help cars self-park following these rules. Q-learning is another important tool that DRL can use to learn about overtaking policies to prevent a collision.

The Bottom Line

Deep Reinforcement Learning is still a topic that requires research to establish how much it can help humanity achieve. Being a branch of artificial intelligence means that DRL is an ongoing trial and error, just to see what is fit for human endeavor. However, we cannot deny the fact that, as of now, artificial learning applies in various departments such as healthcare, engineering, logistics, transportation, etc. This article has just explained a smaller part of the entire giant of DRL as a subject. We hope that this will trigger curiosity into making you dig deeper into this topic. If you’d like to learn more, click here:


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