Introduction to Reinforcement Learning | Data Science - Machine Learning

Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
It is about taking suitable action to maximize reward in a particular situation.
It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.
Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task.
In the absence of a training dataset, it is bound to learn from its experience.