Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge
It addresses the problem of enhancing machine learning models with human expertise, but appears incremental in scope.
This paper examines methods for incorporating domain knowledge into neural network and reinforcement learning models, focusing on encoding human knowledge as logical and mathematical constraints to improve model development.
We present a study of the manners by which Domain information has been incorporated when building models with Neural Networks. Integrating space data is uniquely important to the development of Knowledge understanding model, as well as other fields that aid in understanding information by utilizing the human-machine interface and Reinforcement Learning. On numerous such occasions, machine-based model development may profit essentially from the human information on the world encoded in an adequately exact structure. This paper inspects expansive ways to affect encode such information as sensible and mathematical limitations and portrays methods and results that came to a couple of subcategories under all of those methodologies.