Updated June 14, 2023

Difference Between Supervised Learning and Reinforcement Learning

Supervised Learning is the concept of machine learning which means the process of learning a practice of developing a function by itself by learning from a number of similar examples. This is a process of learning a generalized concept from a few examples provided of similar ones.

Reinforcement Learning is also an area of machine learning based on the concept of behavioral psychology that works on interacting directly with an environment which plays a key component in the area of Artificial Intelligence.

Supervised Learning and Reinforcement Learning comes under the area of Machine Learning, which an American computing professional coined, Arthur Samuel Lee, in 1959, who is an expert in Computer Gaming and Artificial Intelligence.

Machine Learning is a part of Computer Science where the capability of a software system or application will be improved by itself using only data instead of being programmed by programmers or coders.

In Machine Learning, the performance capability or efficiency of a system improves itself by repeatedly performing tasks by using data. Machine Learning also relates to computing, statistics, predictive analytics, etc.

let us understand the difference between Supervised Learning and Reinforcement Learning in detail in this post.

Head To Head Comparision Between Supervised Learning and Reinforcement Learning (Infographics)

Below is the Top 7 comparison between Supervised Learning and Reinforcement Learning:

Key Differences Between Supervised Learning and Reinforcement Learning

Below is the difference between Supervised Learning and Reinforcement Learning:

Supervised Learning has two main tasks called Regression and Classification. In contrast, Reinforcement Learning has different tasks, such as exploitation or exploration, Markov’s decision processes, Policy Learning, Deep Learning, and value learning.
Supervised Learning analyses the training data and produces a generalized formula.
In Supervised Learning, each example will have a pair of input objects and an output with desired values. In contrast, in Reinforcement Learning, Markov’s Decision process means the agent interacts with the environment in discrete steps i.e., the agent makes an observation for every time period “t” and receives a reward for every observation. Finally, the goal is to collect as many rewards as possible to make more observations.
In Supervised Learning, different numbers of algorithms exist with advantages and disadvantages that suit the system requirement. In Reinforcement Learning, Markov’s decision process provides a mathematical framework for modeling and decision-making situations.
The most used learning algorithms for both Supervised learning and Reinforcement learning are linear regression, logistic regression, decision trees, Bayes Algorithm, Support Vector Machines, and Decision trees, etc., those which can be applied in different scenarios.
In Supervised Learning, the goal is to learn the general formula from the given examples by analyzing the given inputs and outputs of a function. In Reinforcement Learning, the goal is in such way like controlling mechanism like control theory, gaming theory, etc., for example, driving a vehicle or playing gaming against another player, etc.,
In Supervised learning, both input and output will be available for decision-making where the learner will be trained on many examples or sample data given. In contrast, in reinforcement learning, sequential decision-making happens, and the next input depends on the decision of the learner or system; examples are like playing chess against an opponent, robotic movement in an environment, and gaming theory.
In Supervised learning, just a generalized model is needed to classify data, whereas, in reinforcement learning, the learner interacts with the environment to extract the output or make decisions, where the single output will be available in the initial state and output will be of many possible solutions.
Supervised learning means the name itself says it is highly supervised. In contrast, reinforcement learning is less supervised. It depends on the learning agent in determining the output solutions by arriving at different possible ways to achieve the best possible solution.
Both Supervised learning and reinforcement learning are used to create and bring some innovations like robots that reflect human behavior and work like a human interacting more with the environment, causing more growth and development to the performance of the system resulting in more technological advancement and growth.

Supervised Learning and Reinforcement Learning Comparison Table

Below is the comparison table between Supervised Learning and Reinforcement Learning.

BASIS FOR COMPARISON	Supervised Learning	Reinforcement learning
Definition	Works on existing or given sample data or examples	Works on interacting with the environment
Preference	Preferred in generalized working mechanisms where routine tasks are required to be done	Preferred in the area of Artificial Intelligence
Area	Comes under the area of Machine Learning	Comes under the area of Machine Learning
Platform	Operated with interactive software systems or applications	Supports and works better in Artificial Intelligence where Human Interaction is prevalent
Generality	Many open source projects are evolving development in this area	More useful in Artificial Intelligence
Algorithm	Many algorithms exist in using this learning	Neither supervised nor unsupervised algorithms are used
Integration	Runs on any platform or with any applications	Runs with any hardware or software devices

Conclusion

Supervised Learning vs Reinforcement Learning is an area of Machine Learning where the analysis of generalized formula for a software system can be achieved by using the training data or examples given to the system.

Reinforcement Learning has a learning agent that interacts with the environment to observe the basic behavior of a human system to achieve the behavioral phenomenon. The applications include control theory, operations research, gaming theory, information theory, etc.,

The applications of supervised and reinforcement learning differ on the purpose or goal of a software system. Both Supervised Learning and Reinforcement Learning have huge advantages in the area of their applications in computer science.

The development of different new algorithms causes more development and improvement of performance and growth of machine learning that will result in sophisticated learning methods in Supervised and reinforcement learning.