Creating safer reward functions for reinforcement learning agents in the gridworld

Abstract

We adapted Goal-Oriented Action planning, a decision-making architecture common in video games into the machine learning world with the objective of creating a safer artificial intelligence. We evaluate it in randomly generated 2D grid-world scenarios and show that this adaptation can create a safer AI that also learns faster than conventional methods.