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Summary:  At the core of modern AI, particularly robotics, and sequential tasks is Reinforcement Learning.  Although RL has been around for many years it has become the third leg of the Machine Learning stool and increasingly important for Data Scientist to know when and how to implement.


If you poled a group of data scientist just a few years back about how many machine learning problem types there are you would almost certainly have gotten a binary response:  problem types were clearly divided into supervised and unsupervised.

  • Supervised: You’ve got labeled data (clearly defined examples).
  • Unsupervised: You’ve got data but it’s not labeled.  See if there’s a structure in there.

Today if you asked that same question you are very likely to find that machine learning problem types are divided into three categories:


While Reinforcement Learning (RL) has been around since at least the 80’s and before that in the behavioral sciences, its introduction as a major player in machine learning reflects it rising importance in AI.

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