Artificial intelligence has taken over the world by storm, and it's no longer a fantasy for machines to learn by themselves. Reinforcement learning and supervised learning are two of the most popular AI learning techniques. They both have their benefits and drawbacks. In this article, we'll look at the differences between reinforcement learning and supervised learning.
What is Reinforcement Learning?
Reinforcement learning is a type of machine learning in which an agent learns to make decisions by trial and error. The agent interacts with its environment by performing actions and receiving feedback in the form of rewards or punishments. The agent tries to maximize its rewards by selecting the actions that lead to the highest reward.
What is Supervised Learning?
Supervised learning is a type of machine learning, where the computer system is trained on labeled input-output data sets. The labeled data set serves for the purpose of predicting unseen outputs, using the input features. The input-output data is fed to the system, and it learns to map the input features to output labels.
Differences between both the forms of Learning
Learning Approach
Supervised learning takes a labeled dataset and tries to find the relationship between the input and output variables, whereas in reinforcement learning, an agent learns by trial and error as it interacts with the environment.
Feedback
In supervised learning, the model receives feedback in the form of the output labels, while the reinforcement learning model receives feedback in the form of rewards or punishments.
Data Requirement
Supervised learning requires a labeled dataset that's used for training and testing, while reinforcement learning doesn't necessarily require labeled data the way supervised learning does.
Time and Cost
Reinforcement learning can be very time-consuming and computationally expensive, while supervised learning is less complex and computational resources are often less expensive.
Performance Metrics
Supervised learning can produce high accuracy when the data is reliable and the model has been trained well, while reinforcement learning achieves optimal performance when it is able to maximize the reward mechanism.
Which one is better?
The answer lies in what you're trying to achieve. There's no clear winner between reinforcement learning and supervised learning as each approach can be more effective for different applications. For instance, if the task involves making data-based predictions, supervised learning is the go-to. But, if the task involves learning from experience over time, reinforcement learning is the way to go.
Conclusion
We hope that you now have a better understanding of the difference between reinforcement learning and supervised learning. Both approaches have their own unique features and applications, and choosing the correct algorithm depends on the specific task you are trying to achieve.
References:
- Adams, Simon. �Reinforcement Learning Vs Supervised Learning: What Is the Difference?� Medium, 20 Apr. 2021, towardsdatascience.com/reinforcement-learning-vs-supervised-learning-what-is-the-difference-afdb2929b2f1.
- Nguyen, Victor. �A Guide to Deep Learning: From Algorithms to Applications.� KDNuggets, 11 Sept. 2020, www.kdnuggets.com/2020/09/guide-deep-learning-algorithms-applications.html.