Supervised Learning vs Reinforcement Learning

Difference Between Supervised Learning and Reinforcement Learning

Supervised Learning is the concept of machine learning that 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 few examples provided those 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 was coined by an American computing professional Arthur Samuel Lee in 1959 who is 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 the 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.

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

Below Is the Top 7 Comparision Between Supervised Learning vs Reinforcement Learning

Key Differences between Supervised Learning vs Reinforcement Learning

Below is the difference between Supervised Learning and Reinforcement Learning

1. Supervised Learning has two main tasks called Regression and Classification whereas Reinforcement Learning has different tasks such as exploitation or exploration, Markov’s decision processes, Policy Learning, Deep Learning and value learning.
2. Supervised Learning analyses the training data and produces a generalized formula, In Reinforcement Learning basic reinforcement is defined in the model Markov’s Decision process.
3. In Supervised Learning, each example will have a pair of input objects and an output with desired values whereas in Reinforcement Learning Markov’s Decision process means the agent interacts with the environment in discrete steps i.e., agent makes an observation for every time period “t” and receives a reward for every observation and finally the goal is to collect as many rewards as possible to make more observations.
4. 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.
5. 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.
6. 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.,
7. 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 whereas 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, gaming theory.
8. 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.
9. Supervised learning means the name itself says it is highly supervised whereas the reinforcement learning is less supervised and depends on the learning agent in determining the output solutions by arriving at different possible ways in order to achieve the best possible solution.
10. Supervised learning makes prediction depending on a class type whereas reinforcement learning is trained as a learning agent where it works as a reward and action system.
11. In Supervised learning, a huge amount of data is required to train the system for arriving at a generalized formula whereas in reinforcement learning the system or learning agent itself creates data on its own to by interacting with the environment.
12. Both Supervised learning and reinforcement learning are used to create and bring some innovations like robots that reflect human behavior and works like a human and interacting more with the environment causes more growth and development to the systems performance results in more technological advancement and growth.

Supervised Learning vs Reinforcement Learning Comparison Table

Conclusion

Supervised 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, this can be achieved only by sample data for training the system.

Reinforcement Learning has a learning agent that interacts with the environment to observe the basic behavior of a human system in order 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 learning as well as reinforcement learning.

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