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Reinforcement Learning in Artificial Intelligence

In this article, we are going to learn about Reinforcement Learning. We will study about the concept of this learning in brief which will include the definition, the methods used by the system in this type of learning to improve its Knowledge Base and will also discuss how a system which works on the reinforcement learning functions.
Submitted by Monika Sharma, on June 18, 2019

Reinforcement Learning is a type of learning method for a computer system or an agent which works on Artificial Intelligence. In this type of learning, the agent learns from the series of rewards or punishments which it gets on the completion of any task. The main aim of this type of agent is to get the maximum rewards. This functionality of this agent helps it to be a utility-based agent because here, the agent chooses the best among the available options so that the user is satisfied completely.

Let us explain this further with the help of an example. Suppose an agent is designed for house cleaning. There are multiple tasks which the agent can do like mopping, dust cleaning, washing utensils, washing clothes. In the agent’s memory, every task is mentioned with different reward points. Suppose we want the agent to work only for some limited amount of time due to the electricity factor or any other factor. It is found that in that period of time, all the mentioned tasks cannot be completed. So, here, the agent will complete those tasks first which will have the highest reward points. It is obvious that the hard and laborious tasks are assigned higher reward points. So, these tasks are automatically completed first by the agent and the leftover tasks are the easy ones which the user can easily do by himself without many efforts if he does not want the agent to work further. So, in this manner, the agent can be used efficiently, and our resources are not wasted and can be used judicially.

However, in Reinforcement Learning, the agent has to keep track of all the actions performed in pasts, their impact on the environment, the reward points secured on performing those actions and the feedback available for those actions. By inferring and learning from these points of the past activities, the agent improves its performance and utility for the future.






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