Introduction to Reinforcement Learning

Machine learning is often divided into three categories: supervised, unsupervised, and reinforcement learning.  Reinforcement learning concerns problems with sequences of decisions (where each decision affects subsequent opportunities), in which the effects can be uncertain, and with potentially long-term goals.  It has achieved immense success in various different fields, especially AI/Robotics and Operations Research, by providing a framework for learning from interactions with an environment and feedback in the form of rewards and penalties.

Shane Conway, researcher at Kepos Capital, gives a general overview of reinforcement learning, covering how to solve cases where there is uncertainty both in actions and states, as well as where the state space is very large.  

01:15:47

This talk was given at the New York Open Statistical Programming Meetup.