Review on use of Reinforcement Learning in Artificial Intelligence

Résumé ou Introduction

Human started
making machinery that can do the job for them. The
technology developed so much that it started involving
many other branches of engineering such as electronics,
robotics etc. This eventually led to much more complex
and smart machinery involving Artificial Intelligence.
Reinforcement Learning is a type of Machine Learning,
and thereby also a branch of Artificial Intelligence. It
allows machines and software agents to automatically
determine the ideal behavior within a specific context, in
order to maximize its performance. Reinforcement
Learning (RL) comes from the animal learning theory.
RL does not need prior knowledge, it can autonomously
get optional policy with the knowledge obtained by trial-
and-error and continuously interact with dynamic
environment.
As a matter of fact, Reinforcement Learning is defined by
a specific type of problem, and all its solutions are classed
as Reinforcement Learning algorithms. In the problem,
an agent is supposed decide the best action to select based
on its current state. When this step is repeated, the
problem is known as a Markov Decision Process.
A Markov Decision Process is a discrete time stochastic
control process. At each time step, the process is in some
state s, and the decision maker may choose any action
that is available in state‘s’. Markov Decision Process
provides a mathematical framework for modeling
decision-making in situations where outcomes are partly
random and partly under the control of a decision maker.