Understanding Reinforcement Learning

Machine learning algorithms can be broadly, divided into four main types:

• Supervised learning algorithms
• Semi-supervised learning algorithms
• Unsupervised learning algorithms
• Reinforcement learning algorithms
  

In this blog, we will have a look at reinforcement learning algorithms.

What is reinforcement learning?

Reinforcement learning is a learning algorithm used to train the model based on rewards and punishment. The algorithm is programmed to choose actions that maximize rewards.

Let's have a look at a simple example to understand reinforcement learning better.

Suppose you have a dog at home, and you are training them to obey your orders. Every time they do, you reward them with a treat. And every time they disobey, you punish them by keeping them deprived of the goodies.

This concept, when applied to machine learning algorithms, is termed reinforcement learning.

What are the types of reinforcement learning algorithms?

There are two main types of reinforcement learning based on the algorithms actions to earn the rewards. They are:

Positive reinforcement learning

This type of algorithm is programmed to increase the strength and performance of the algorithm by taking positive actions. Example: Online recommendation models use good reinforcement algorithms wherein the algorithms are designed to get rewarded every time the user clicks on a recommended item.

Negative reinforcement learning

This type of algorithm is programmed to increase the strength and performance of the algorithm by reducing or avoiding negative actions. Example: Autonomous cars are programmed with negative reinforcement algorithms, and the algorithms are rewarded and strengthened every time they avoid collisions.

What are the limitations of reinforcement learning algorithms?

Although reinforcement learning algorithms can create numerous, unique, and valuable programs, they come with certain caveats that should be considered before choosing an algorithm to design a program. They are:

• They are based on a trial-and-error approach.
• They are time-consuming.
• They require a lot of data and a lot of computational power to get trained.
  

Although reinforcement learning has certain limitations, it can transform the world of machine learning and artificial intelligence and enable us to create new, unimaginable, and innovative machine learning programs. 

To read more articles related to artificial intelligence and machine learning, visit the Cogent Infotech website.