A Machine Learning Model for Training Your AI

Abstract

Artificial Intelligence is playing an increasing role in solving some of the world’s biggest problems. Machine Learning Models, within the context of reinforcement learning, define and structure a problem in a format that can be used to learn about an environment in order to find an optimal solution. This includes the states, actions, rewards, and other elements in a learning environment. This also includes the logic and policies that guide learning agents to an optimal or nearly optimal solution to the problem. This paper outlines a process for developing machine learning models. The process is extensible and can be applied to solve various problems. This includes a process for implementing data models using multi-dimensional arrays for efficient data processing. We include an evaluation of learning policies, assessing their performance relative to manual and automated approaches.