Comparing Reinforcement Learning and Human Learning With the Game of Hidden Rules

Abstract

Human-machine systems, especially those involving reinforcement learning (RL), are becoming increasingly common across application domains. Designing these systems to be effective and reliable requires a task-oriented understanding of both human learning (HL) and RL. In particular, how does the structure of a learning task affect the learning performance of humans and RL algorithms? Games and other learning environments can serve as important tools in this line of research. While a trend toward increasingly complex environments has led to improved RL capabilities, such environments are difficult to use for the dedicated study of task structure for humans and algorithms. To address this gap we present a novel learning environment called the Game of Hidden Rules (GOHR), built to support rigorous study of the impact of task structure on HL and RL. We demonstrate the GOHR's utility for such study through example experiments where humans and learning algorithms display opposite responses in performance to tested variations in task structure.