Reinforcement Learning is a type of learning process aimed at making decisions in an environment to maximize the amount of reward obtained. It is a trial-and-error method. The most distinctive features of reinforcement learning are trial-and-error and reward.

In supervised and unsupervised learning, an agent is directly taught, but here it is done indirectly. This indirect process is carried out through some rewards. For example, consider a lion playing a ring game. If the lion goes through the ring from one side to the other, it gets food. If it goes outside the ring, it doesn’t get food. Here, the lion is not directly taught what to do but learns indirectly through rewards. The food acts as the reward.

If we imagine a state machine, there are two events present: an agent and an environment. The agent always learns from the environment and continuously improves its decision-making ability.

Is Reinforcement Learning Supervised or Unsupervised?

In short, it is both. Reinforcement Learning uses both supervised and unsupervised learning techniques.

Supervised Learning and Reinforcement Learning

In supervised learning, data is labeled, meaning the identity of the data is recorded. A model is created from this known data. After creating the model in supervised learning, it can make decisions for similar or categorical unknown data but cannot handle new situations. In this case, reinforcement learning uses exploitation and exploration techniques to make decisions.

Unsupervised Learning and Reinforcement Learning

After reading the previous paragraph, you might think that it is unsupervised learning. But that’s not entirely true either. In unsupervised learning, data is unlabeled. The main goal of unsupervised learning is to separate and categorize similar types of data from this unlabeled data. However, in reinforcement learning, the goal is not to find categories or hidden patterns but to maximize the reward.

For this reason, reinforcement learning is given a third category and is sometimes called semi-supervised learning.

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