Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

Machine learning acts as the basis for various high-end technologies and different sub-types. For instance, deep learning and reinforcement learning are common types of machine learning that help automate the machine’s learning process. In this article, you will learn about reinforcement and how this technology is helping:

What is Reinforcement Learning?

In simple words, we can say that reinforcement learning is one of the techniques of machine learning. You can train an artificial intelligence agent by allowing them to perform repetitive actions and rewarding it. The agent in the experiment of reinforcement learning will take various actions. On correct actions, the agent will receive rewards. However, with wrong actions, they will receive punishment. This will increase the learning capability of the agent for taking the actions.

If we look into the detailed psychological definition of the RL, we can understand even more. The term reinforcement refers to something that increases the chances to progress in any task or action. Reinforcement learning, according to this concept, means that anything that helps to improve behavior.

For instance, if we think of reinforcement in humans, we have various rewards such as raise, bonus, praise, a gift, or any fun activity that boosts performance. Similarly, when your dog is behaving properly, you can give them a treat. This response is positive reinforcement. However, when you shout at your cat for their jumpy behavior, that is negative behavior. That helps in removing the behavior of your pet.

How is Reinforcement Learning Different from Machine and Deep Learning?

This can be a tricky question as there is no clear reason to divide reinforcement, deep learning, and machine learning. These are just like all the corners of a triangle. Machine learning is the top category, and the subtypes are deep learning and reinforcement learning.

The function of RL is the same as deep learning and machine learning. However, the application is specialized and has a particular method to solve complex problems. Many people will consider that the concept of all the ideas is different, but we cannot divide these technologies.

On many projects, you can also merge the technologies to perform the task productively and effectively and yield quick and high positive results.

· Machine Learning

Machine learning is one form of artificial intelligence. It has the ability to improve the progressive performance of a task with the help of data set without programming. Machine learning falls into two types. Supervised machine learning is the first type, while unsupervised machine learning is the second.

· Deep Learning

Deep learning includes an additional hidden layer in the neural networks. These hidden layers can handle complicated tasks. The model of deep learning is similar to the human brain’s functionality when solving problems. This technology only works for a particular task with given data as there are few layers of artificial neural networks.

Solving the Problems with Reinforcement Learning

Now various industries and business automate their tasks through different technologies. Reinforcement learning is a strong algorithm that can solve numerous problems and perform tasks without human efforts. Here are some of the complex problems that we can solve through RL:

1. Manufacturing Process

Reinforcement learning can minimize the human efforts and labor cost and time in manufacturing tasks. Various high-tech companies are developing robots that can self-learn the process and tasks with more speed, high accuracy, and less effort.

2. Real-time Advertising and Bidding

Various bidding agents can use workplace advertisements for their products or services according to market understanding and analyzing various other advertisements. Reinforcement learning can handle strategies for advertisement with more user engagement and higher accuracy. Furthermore, the feedback from customers also gathers through more than a single agent for adjustments. A group of agents can help develop more accurate results instead of a single agent.

3. Personalized Recommendations for News

There are limitations to personalizing the news. The challenges of new recommendations make people boring and less interested. Reinforcement Learning helps predict the user’s preferences through a reward-oriented framework according to user feedback.

4. Resource Optimization

The management tasks, such as awaiting jobs, can be time-consuming. However, with an effective algorithm of reinforcement learning, available jobs can be allocated in less time.

5. Auto-Configuration

For the web system’s performance and speed, auto-configuration is an essential component to deal with internet traffic. With the help of reinforcement learning, you can reduce the learning time by enhancing the initiation and auto-adapting the parameter to improve performance.

Future Advancement of Reinforcement Learning

The best thing about reinforcement learning that indicates that this machine learning tool’s future is bright as it can help develop models with complex tasks through a simple single model. Deep learning helps solve problems that we used to face. In the future, RL can help us more in various advanced activities. Through reinforcement learning, you can develop new solutions by training the Artificial intelligence agent. The possibilities of using RL are vast. However, some of the future applications that we can develop in the near future are:

  • Prosthetic limb
  • Autonomous robots
  • Advanced self-driving
  • Entirely automated factories


Reinforcement learning includes training of the agent that is the essential part—however, other factors, such as the environment and the data provided to the agent for inspecting. The agent will then try to find the maximum reward by applying different methods and patterns. An artificial neural network is responsible for storing the data and improving the task’s performance through experience for deep reinforcement learning.