What is Reinforcement Learning?

Reinforcement learning is an ML strategy that rewards desirable actions while penalizing undesirable ones. A reinforcement learning agent can sense and comprehend its surroundings, act, and learn via trial and error.

How can it learn? Developers establish a system of rewarding desired actions and punishing bad ones in reinforcement learning. This strategy motivates the agent, providing positive values to desired acts and negative values to undesirable behaviors. To obtain an ideal solution, the agent is programmed to seek long-term and greatest overall return.

These long-term objectives keep the agent from stagnating on smaller goals. The agent eventually learns to shun the unpleasant and focus on the good.

  • This type of learning has been used in AI to drive unsupervised machine learning using incentives and punishments.


While reinforcement learning has sparked a lot of interest in the field of AI, it still has a long way to go in terms of general acceptance and application. Despite this, research articles on theoretical applications abound, and several compelling use cases have been documented. Examples of current use cases include gaming, robotics, resource management, and personalized recommendations.

The most prevalent use of reinforcement learning is probably gaming. In a variety of games, it is possible to obtain superhuman performance.

  • Reinforcement learning may work in any scenario as long as there is a clear reward!

Reinforcement learning algorithms in resource management may distribute limited resources to diverse activities as long as there is an overarching objective to pursue. In this situation, one purpose may be to save time or resources.

Reinforcement learning has made its way into limited experiments in robots. This sort of machine learning can enable robots to learn activities that a human instructor is unable to show, transfer an acquired skill to a new assignment, and achieve optimization despite a lack of analytic formulation.

Control, game and information theory, based optimization, statistics, and operation research all incorporate reinforcement learning.


While reinforcement learning has a lot of potential, it’s difficult to put into practice and has limited applications. One of the challenges in deploying this form of machine learning is its dependency on environment investigation.

If you deploy a robot that relies on reinforcement learning to navigate a complicated physical environment, for example, it will seek out new states and perform different behaviors as it advances. However, because the environment changes so quickly in the actual world, it is impossible to continuously execute the appropriate choices.

The time necessary to guarantee that this technology is used correctly might restrict its utility and use a lot of processing resources. Demands on time and compute resources increase as the training environment becomes more sophisticated.

If the right quantity of data is obtainable, supervised learning may provide firms with faster, more efficient outcomes than reinforcement learning since it can be used with fewer resources.

Reinforcement learning vs supervised and unsupervised learning

Reinforcement learning is a distinct field of ML, however, it has certain characteristics with other forms of ML:

Supervised learning – Algorithms in supervised learning are trained on a set of labeled data. Only the qualities given in the data set may be learned by supervised learning algorithms. Image recognition models are a common application of supervised learning. These models are given a set of annotated photos and are taught to recognize common characteristics of preset shapes.

Unsupervised learning – Developers use unsupervised learning to let algorithms free on completely unlabeled data. Without being instructed what to search for, the algorithm learns by documenting its observations regarding data properties.

Reinforcement learning – Type of learning that uses positive reinforcement. This takes a very different approach. It places an agent in a setting with explicit constraints defining productive and non beneficial conduct, as well as an overarching goal to achieve. Developers must provide algorithms with well-defined goals and specify rewards and punishments, which is akin to supervised learning in certain aspects. This means that supervised learning needs more explicit programming than unsupervised learning. However, once these parameters are set, the algorithm is self-directed, unlike supervised learning algorithms. As a result, reinforcement learning is occasionally referred to as a subset of semi supervised learning, but it is more commonly recognized as a unique sort of machine learning.