Markov Decision Process

Reinforcement Learning

Reinforcement Learning is a type of Machine Learning. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal.

There are many different algorithms that tackle this issue. 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 to decide the best action to select based on his current state. When this step is repeated, the problem is known as a Markov Decision Process.

A Markov Decision Process (MDP) model contains:

  • A set of possible world states S.
  • A set of Models.
  • A set of possible actions A.
  • A real-valued reward function R(s,a).
  • A policy the solution of Markov Decision Process.