What is Reinforcement Learning?

We are living in the 21st century, the era of automation. Machine Learning has been a rock band in the field of automation. The automated machines that we create using the techniques of Machine Learning carry out iterative tasks to reduce human effort and time.

However, the real-world tasks are way too complex for a machine to execute. It is a highly redundant task to program every course of action for a machine. There emerges the need for a technique that enables the machine to learn and improve itself. This Machine Learning technique is called reinforcement learning.

Reinforcement learning in Machine Learning is a technique where a machine learns to determine the right step based on the results of the previous steps in similar circumstances.

Mechanism of Reinforcement Learning

• Reinforcement learning works on the principle of feedback and improvement.
• In reinforcement learning, we do not use datasets for training the model.
• Instead, the machine takes certain steps on its own, analyzes the feedback, and then tries to improve its next step to get the best outcome.

Reinforcement Learning Process

Reinforcement learning is the craftsmanship of devising optimal judgments for a machine using experiences. Splitting it further, the method of reinforcement learning includes the following steps:

1. Investigating circumstances
2. Deciding an action by applying some tactics
3. Performing the action
4. Obtaining a reward or punishment
5. Discovering new areas with the help of past experiences and improving the approach
6. Iteratively sticking to the strategy and performing the action until the machine learns properly