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.
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