Difference between Deep Learning and Reinforcement Learning

Our level of artificial intelligence (AI) maturity, as well as the types of challenges that AI might be able to assist us in resolving, grows in tandem with the ever-increasing volume of data that we produce. This data, along with the incredible computing power that is now available for a price that is affordable, is what is fuels the tremendous growth that has been seen in AI technologies, and it is also what makes deep learning and reinforcement learning possible. In this article, I will explain the difference between Deep Learning and Reinforcement Learning by providing definitions that are clear and easy to comprehend for both of these learning methods.

Deep Learning and Reinforcement Learning are closely related to the amount of computing power that artificial intelligence possesses. These self-sufficient functions of machine learning pave the way for computers to devise their own guiding principles when coming up with solutions to problems.

Deep Learning makes use of previously collected data, whereas Reinforcement Learning relies on the tried-and-true method of learning through experimentation.

What is Deep Learning?

Rina Dechter, a professor of computer science at the time, was the person who first presented the idea of Deep Learning in the year 1986.

Deep Learning utilizes the most recent information available in order to teach algorithms how to search for relevant patterns, which is essential when forecasting data. A system like this makes use of various levels of artificial neural networks (ANNs) that are constructed in a manner analogous to the neuronal composition of the human brain. It's possible that the algorithm will be able to process millions of pieces of data and zero in on a more precise prediction if it has access to complex links.

When programmers want a piece of software to recognize the color violet in a variety of images, they might use this kind of learning to train the software. After that, a variety of pictures some with violet colors and some without would be presented to the computer program as part of the "deep learning" process. The software will be able to recognize patterns and figure out when to mark a color as violet if it is given the opportunity to do so through clustering.

Deep learning is utilized in a variety of recognition programmes including image analyses as well as forecasting tasks including time series predictions.

What is Reinforcement Learning?

Generally speaking, reinforcement learning will perform the actions in order to maximize the rewards. To put it another way, learning is the process of engaging in activities with the intention of obtaining beneficial consequences. This is very similar to how we learn things like how to ride bikes, where we have to experience failure in order to succeed.

Using the feedback from users, both what didn't work and what did work, we were able to fine-tune the action and grasp required to ride a bike. In the same way, computers use learning by reinforcement and try out a variety of actions; then, based on the feedback they receive, they learn and, ultimately, reinforce the actions that are successful.

Let's take an example. One of the applications of the algorithm, which is known as reinforcement learning, is a robot that is teaching itself how to walk. At first, a robot that is sufficiently sized to take a step forward attempts to do so but fails.

The outcome of the fall is a data point that represents a significant advancement in the way the system responds to reinforcement learning. Because the fall is an outcome that worked as negative feedback to adjust the system to attempt a smaller step, it was necessary for it to occur. At long last, the robot has gained the ability to proceed forward.

Difference between Deep Learning and Reinforcement Learning

The following table highlights the major differences between Deep Learning and Reinforcement Learning −

Basis of ComparisonDeep LearningReinforcement learning
OriginThe concept of Deep Learning was introduced in the year 1986 by Rina Dechter.Reinforcement Learning was developed in the late 1980s by Richard Bellman.
UtilizationIn the areas of speech and picture recognition, the dimension reduction task, and the pretraining for deep networks.Specifically, in the fields of robotics, computer gaming, telecommunications, AI in healthcare, and elevator scheduling.
Data ExistenceData set that is already present and necessary for learning.Because it is exploratory in nature, it does not require an existing data collection for the purpose of learning.
Comparison with Human BrainReinforcement learning is a type of artificial intelligence that can be enhanced by the use of feedback, making it more comparable to the capabilities of the human brain than deep learning.Deep learning is focused mostly on recognition and has a weaker connection to interactive learning.
Method of InstructionDeep learning can perform the desired action by first doing an analysis of previously collected data, and then applying the knowledge gained to a fresh collection of data.The answer can be altered through the use of reinforcement learning, which adapts to continual input.
ApplicationsImage and speech recognition, as well as deep network pre-training and dimension reduction are all examples of applications for deep learning.In contrast, reinforcement learning is applied in situations where optimal control is required when dealing with external stimuli. Some examples of this include robotics, elevator scheduling, telecommunications, computer games, and artificial intelligence in healthcare.
Also calledDeep learning is also referred to as hierarchical learning or deep structured learning.Reinforcement learning has no other commonly used terms.


Deep learning, also known as reinforcement learning, and computational learning are highly connected with one another. Deep learning has its beginnings in 1986, when Rina Dechter first presented the concept to the academic world. Reinforcement learning, on the other hand, can be dated back to the late 1980s and was first presented by Richard Bellman.

Deep learning is utilized in tasks including dimension reduction, speech and picture recognition, and pretraining for deep networking. On the other hand, reinforcement learning can be implemented in fields such as robotics, computer gaming, telecommunications, artificial intelligence in healthcare, and elevator scheduling.