Enabling self-sufficient agents that influence their environments to train optimal behaviors and enhancing over time through trial and error is a primary goal of Artificial intelligence. It is a challenging task to design working AI systems that can learn productively. Robots capable of sensing and providing responses to the world around are the original software-based agents that interact with multimedia and natural language. Let’s learn more about reinforcement learning.
In recent years, a high rise has occurred in Deep Learning, which is built on the computer-powered functions of representation, approximation, and Neural Networks. This rise has provided new tools to overcome the low dimension’s scalability problems. Reinforcement Learning (RL) is a subset of Machine Learning that improves the algorithm’s quality via continuous learning. Now, let’s focus on getting to know more about Reinforcement Learning (RL) and its applications.
Reinforcement Learning is a branch of Machine Learning, which involves training without any human interaction. In this section, we will discuss Reinforcement Learning with an example. It has an autonomous agent that trains how to interact in an environment by executing actions. The training of algorithms is based upon the output to obtain the required goal. Based on the function type, it is specified as a Positive or a Negative learning method. It is a principle mathematical framework designed to autonomously learn based on various experiences and challenges in simpler terms. Let us find a solution to the following query – What do you mean by Reinforcement Learning (RL)?
The following Reinforcement Learning examples provide a clear understanding of the topic. Here, a family wants to train the baby to walk around the house.
There is a year-old baby in the house, and the family is training the baby to walk. So, the family has set a goal for the baby to reach the other end of the room without their help. They will stand at the other end, calling the baby.
The baby will be rewarded with chocolate upon success. Now the baby attempts to walk to attain this reward. Similarly, the RL algorithms train to earn rewards or points. If the baby walks to the end of the room, the trainer (family) and learning baby would be happy.
Here, there is a couch in the middle of the path. Now, while walking, the baby will try to climb the couch and may get hurt. Based on the incident, the baby will learn that climbing is dangerous, and the baby will not attempt it next time.
There are two types of Reinforcement Learning methods – Positive and Negative.
It occurs only because specific behavior is triggered. It increases the agent’s frequency and strength. The impact on the agent’s decision is positive.
Pros: This type of Reinforcement Learning aids in improving performance and achieving sustained change for a longer period.
Cons: Excessive Reinforcement may cause a higher-optimization of state changing the results.
Negative Reinforcement strengthens the behavior that occurs based on the negative condition that should be avoided.
Pros: This aids in defining the minimum standard of performance.
Cons: Only the lowest behavior of the model is achieved by using Negative Reinforcement Learning.
There is a wide range of applications of Reinforcement Learnings.
It is mandatory to highlight some challenges in Reinforcement Learning. Let us have a look at them below:
As many past approaches are still used, the challenges of scalability and fairly low-dimensional problems persist. Reinforcement Learning algorithms have the same complex issues as other algorithms like Computational Complexity, Memory Complexity, and Machine Learning algorithms Complexity issues.
Reinforcement Learning (RL) is a key creation in industrial AI systems that interact with the subject environment and train from them. If you are interested in learning more about Artificial Intelligence, Machine Learning, and Reinforcement Learning, check out our Postgraduate Certificate Program in Artificial Intelligence & Deep Learning program.