What Is Machine Learning? How Reinforced Learning Differs From Supervised Learning?

A person who begins machine learning is often confused between supervised learning and reinforced learning. While in general, Machine Learning focuses on the development of computer programs that can access data and use it to learn for themselves, both supervised and reinforced models are used for varied purposes. The former is a more commonly used form than reinforced learning because it's a faster, cheaper than the other. Also, the latter requires a learning agent to learn from the environment rather than being guided on what to do. In other words, an abstract definition would be that supervised learning requires labeled data is fed to ML algorithms, i.e., mapping from the input to the essential output, under the presence of a supervisor. In comparison, reinforced learning can tell a Computer if it has made the correct decision or the wrong decision. It is all about developing a self-sustained system that can improve itself throughout contiguous sequences of tries and fails on the basis of the combination of labeled data and interactions with the incoming data.

Examples of supervised learning include linear regression, logistic regression, k-nearest neighbors, support vector machines, decision trees, and random forests and Neural networks. And examples of reinforced learning are Q-Learning, asynchronous actor-critic agents (A3C), temporal difference (TD), and Monte-Carlo tree search (MCTS).

In supervised learning, tasks can be categorized into regression and classification. Regression is the problem of estimating or predicting a continuous quantity, whereas classification is about assigning observations into discrete categories, rather than estimating continuous quantities. And in reinforced, the technique used is called exploration or exploitation. In this method, first, the action takes place, the consequences are observed, and then the next action is based on the outcome of the first action. Further, in reinforcement learning, developers run a method for the machine to quantify its performance in the form of a reward signal. This can be positive or negative. In a positive reward signal, the continuation of a particular sequence of action is encouraged. In contrast, the negative reward signal imposes penalty for performing certain activities and urges to modify the algorithm to prevent the chances of getting penalties.

Reinforced learning, explicitly considers the whole problem of an objective oriented problem, while constantly interacting with an unknown and uncertain environment in discrete steps. This approach is termed as Markov's Decision. According to experts, this methodology is much better in comparison with supervised learning as it considers sub-problems without bearing in mind how they might fit in the larger picture. Also, unlike like labeled data in supervised learning, it does not need any predefined data too. While the supervised learning aims to generate formula based on input and output values, reinforced learning learns through a series of actions. Supervised learning has applications in the weather forecast, pricing prediction, identifying customer satisfaction levels, risk evaluation, and others. And reinforced learning is used for gaming, healthcare, self-driving cars, and much more.