Reinforcement Machine Learning

 

Reinforcement learning is a type os learning which allows machine to determine the ideal behaviour automatically under some specific limits so as to maximize the presentation. It is basically tells about the interaction between two components that is the enivironment and the learning surrogate which holds two process – exploration and other is exploitation.

When the machine works on the trail basis, it is known as Exploration and when the machine works on the basis of knowledge, it is known as exploitation.

Here, the reward is given for correct presentation to the agent  which is called as Reinforcement Levels. Surrogating the rewards earned by agent improves the environment of machine to check the next action. In contrast, it gets penalty for wrong presentation

 

 

 

Some of the popular  uses of Reinforcement Learning are –

3.3.1Banking Sector – It is used to create the best offers for call centers based on the past expiriences of  users that is by their trials.

3.3.2.Healthcare-  It is used to allocate the scars medical to handle different types of ER cases

 

Supervised learning have some advantages over unsupervised learning, but also they have some limitations. The system are more likely to make decisions that human can relate to, because humans have provided the basis for judgements.