Category: Machine Learning

In the history of engineering and machine learning, choosing transparent models that are interpretable for humans or end-users is essential. Practically, it means using transparent data sources and simple and easy to interpret models like linear models and decision trees or even rule-based systems despite their limitations due to real-world scenarios where observations are nonlinear and very specific. With the massive growth of machine learning and deep learning popularity, model complexity, and the spread of AI in all fields, it …

Reinforcement learning (Sutton & Barto, 1998) is a formal mathematical framework in which an agent manipulates its environment through a series of actions and in response to each action receives a reward value. The agent will try to maximize results by choosing the best reward with the internal agent. In the fact, the agent aims to get a maximum reward over time. The agent is not taught to decide which road chooses but some signals are given to him to …