Reinforcement learning can be defines as a feedback-based machine learning technique. In which an agent learns from the feedback to make decisions accordingly by interaction with the environment. After execution provide feedback, for positive feedbacks, the agent wins rewards, but if it performs badly, it gets negative feedback.

Example:

Self-Driving Cars or Autonomous Vehicles : Autonomous vehicles use Reinforcement Learning most frequently to make real-time decisions, like lane changes and obstacle avoidance, by learning from vast amounts of driving data.

Types of Reinforcement Learning

Two categories of reinforcement learning techniques exist:-

Positive:

It can be defined as an outcome generated from the particular event or actions. It has a beneficial impact on the agent’s action and raises the intensity and frequency of the behavior. This enhancement in behaviour will help to maximize the performance and maintain the changes for longer period.

Negative:

Negative reinforcement involves reinforcement of the behavior that arises because of a negative condition. It will help to define a minimum performance level. However, the drawback of this method is that it only provides enough to meet the minimum behavior.

Summary:  Reinforcement Learning enables agents to learn optimal behaviors through interaction with their environment, adjusting their actions based on received rewards or punishments. This learning process mimics how humans and animals learns from their surroundings, making RL a powerful tool in AI.