Hello, folks! In this article, we will be having a look at reinforcement learning in the field of Data Science and Machine Learning.
Machine Learning as a domain consists of variety of algorithms to train and build a model for prediction or production. On a large scale basis, there are three types of ML algorithms:
- Supervised ML
- Unsupervised ML
- Reinforcement Learning
Today, we will be focusing on Reinforcement Learning in the upcoming sections. So, let us begin!
What is Reinforcement Learning?
Reinforcement Learning is a type of ML algorithm, wherein, it teaches the system or the environment to learn from the agent provided. The learning agent reads the decisions and patterns through trial and error method without having an idea of the output.
So, here, the system is not fed with any understanding of the output, unlike, Supervised ML wherein the output is fed to the system to learn the patterns and recognize the upcoming data.
In reinforcement learning, the main goal is to find the suitable model that would eventually maximize the overall chances of the agent to learn in a correct manner and predict the outcome.
Thus, it makes a sequence of decisions and the error is fed back to the model by the learning agent.
Let us now jump into the working of the model in detail.
Important terms in Reinforcement Learning
Have a look at the below model!
- Environment: Space or the system to operate within.
- Reward: Feedback received from the agent to work upon.
- State: Present situation of the agent in the environment.
From the above pictorial representation, it is clear that the agent works on the input data, initially on a trial and error basis. Further, the reward is fed back to the system to maximize the correctness of the goal to be achieved.
By this, the model continues to learn and grow and the best outcome is predicted on the basis of the maximum reward obtained.
Types of Reinforcement Learning
In the current state of Machine learning, there are two major types of reinforcements:
1. Positive Reinforcement Learning
In this type of learning, any reaction generated due to the action and reward from the agent increases the frequency of a particular behavior and thus has a positive effect on the behavior in terms of output.
2. Negative Reinforcement Learning
Here, any reaction because of the reward/agent would reduce the frequency of a certain set of behavior and thus would have a negative impact on the output in terms of prediction.
Applications of Reinforcement Learning
- To generate recommendation systems based on the initial inputs of taste or genre.
- In the domain of Robotics, to trace paths or for the purpose of automation.
- In the gaming industry, to decide the moves or outcome in accordance with the conditions selected by the player.
- Text/Speech interaction system which learns from the user interactions and dialogues.
By this, we have come to the end of this topic. Feel free to comment below, in case you come across any question.
Till then, Stay tuned @ ML with JournalDev and Keep Learning!!