Reinforcement learning is an area of machine learning which works on reward-based models for prediction and decision making. It deploys a feedback-based mechanism to reward a machine when it MAKES good decisions. Negative feedback is provided to the machine when it does not perform well. This encourages the machine to find the best possible behavior in a particular situation. Unlike supervised learning, the agent learns autonomously utilizing feedback and no labelled data in Reinforcement Learning. Because there is no labeled data, the agent MUST rely only on its own experience to learn.  RL is used to tackle a certain sort of problem in which sequential decision-making is required and the aim is long-term, such as game-playing, robotics, and so on. The agent interacts with and explores the world on its own. In reinforcement learning, an agent's primary goal is to increase PERFORMANCE by obtaining the most positive rewards. The agent learns through trial and error, and as a result of its experience, it improves its ability to complete the task.

Reinforcement learning can be best understood by taking an example of a dog. When the owner of the dog wants to cultivate a good habit in his dog, he will train his dog to do that thing with the help of a treat. The dog will be rewarded with the treat if he obeys his owner. If he disobeys the owner, the owner will use a negative reinforcement technique by not giving his dog his favorite treat. This way, the dog will associate the habit with the treat. This is precisely how reinforcement learning works in a machine.

Applying Reinforcement learning Principles to dogs. Repeatedly awarding the dog with treats (positive reinforcement) can MAKE the dog adapt to the good habits (walking in this case) quickly.