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Reinforcement Learning and Essential Aspects

Reinforcement Learning and Essential Aspects

Reinforcement Learning refers to a Machine Learning procedure that helps a software agent by suggesting what actions he must take in any given environment. It forms a part of deep reinforcement learning that emphasizes maximizing the cumulative rewards. 

It is a neural network learning method that will help you to achieve a complex goal or objective. 

More about reinforcement learning

Before getting into the depth, few terms will help you to understand the concept better. These are as follows-

  • Agent- Refers to an entity that acts to get a reward.
  • Environment-A specific scenario that the agent is or encounters. 
  • Reward –A prompt return that you give an agent once his performance is over. 
  • Action- The step the agent needs to take to address a situation

Many other terms are used to explain the concept. But we will use just the 4 mentioned above in our discussion. 

Let us find out more about the same under the following sub-topics, 

  • Reinforcement learning applications
  • Why apply Reinforcement Learning
  • When should you not apply Reinforcement Learning?

So, let us take one sub-topic at a time below. 

1. Reinforcement learning applications

As far as the application of the concept and algorithm are concerned, you can expect to find its application in the following areas-

  • Data processing
  • Machine learning
  • Robot motion control
  • Aircraft control
  • Planning out business strategies
  • Robotics related to industrial automation

2. Why apply Reinforcement Learning

The reasons for using this type of learning are many. Some of the prominent reasons are as follows-

  • This method will help you to assess which situation requires an action
  • It will also help you to identify the action that will give you the highest returns over a while. 

3. When should you not apply Reinforcement Learning?

Although it helps you identify the best method to apply for maximum gains; and the instant reward you can give an agent for the action, yet there are few instances when using the procedure is not a wise decision.

These instances are-

  • If you have adequate data to address any problem, you can go ahead with the supervised learning method. 
  • Remember, in the event, the action area is quite large, the process is cumbersome and time-consuming. 

In a nutshell, being a Machine Learning method, Reinforcement Learning works by interacting with the environment whereas, in the case of the supervised learning method, it works on a bulk sample or given data. 

Reinforcement Learning involves 3 methods, namely, Value-based, Model-based, and Policy-based learning. Apply it for planning out your business strategies depending on how perfect it will be for meeting your organizational goals and mission.

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