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Difference Between Supervised Learning & Reinforcement Learning

An American computing professional Arthur Samuel Lee said that supervised learning and reinforcement learning are the types of machine learning.
Machine Learning:
Supervised learning is a concept of machine learning that means a process of learning by itself from similar examples.
Reinforcement Learning:
It is also a type of machine learning, and the system learns through the psychological behavior of the environment. It is a fundamental component of artificial intelligence technology.
Key Differences Between Machine Learning and Reinforcement Learning:

    • Supervised learning has two main functions called classification and regression, whereas reinforcement learning has different functions such as policy learning, exploration, value learning, and deep learning.
    • Supervised learning makes analysis of the training data and generate a generalized formula. In reinforcement learning, the model Markov’s decision process defines fundamental reinforcements.
    • In supervised learning, each object will have a pair of input objects and an output with desired values. In reinforcement learning, Markov’s Decision process defines that the agent interacts with the environment in discrete steps.
    • In supervised learning, different numbers of algorithms exist with advantages and disadvantages that suit the system needs. In reinforcement learning, Markov’s decision process provides a mathematical framework for modeling and decision making positions.
    • The most used learning algorithms for both supervised learning and reinforcement learning are linear, logistic regression, decision trees, Bayes algorithm, and those which you can apply in different situations.
    • 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 a way as controlling mechanisms like control theory, gaming theory, etc., for example, driving a vehicle or playing gaming against another player, etc.
    • In supervised learning, you only need a generalized model to classify data. But in reinforcement learning, the learner interacts with the environment to extract the output or make decisions, where the single answer will be available in the initial state and output, which will be of many possible solutions.
    • Supervised learning makes predictions depending on a class type. But reinforcement learning trains itself as a learning agent where it works as a reward and action system.
    • In supervised learning, the system needs a large amount of data to train itself for arriving at a generalized formula. Whereas in reinforcement learning, the system or learning agent itself creates data on its own by interacting with the environment.
  • Professionals are using both supervised learning and reinforcement learning to create and bring some innovations like a robot. It reflects human behavior and works as a human, and interacting more with the environment causes more growth and development to the systems performance results in more technological advancement and growth.


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