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.
More about reinforcement learning
- 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.