Examining Conditions for Emergence of Observational Learning in Reinforcement Learning Agents

Abstract

Observational learning is a method of learning by observing the behavior of others. While the mechanisms and conditions for the emergence of observational learning have been studied using biological approaches, research methods using computational models have attracted attention in recent years. However, previous research using computational modeling has been limited to experiments on specific types of observational learning. In this study, we examined conditions for the emergence of more complex observational learning from two perspectives: the external conditions, such as the environment and reward values, and the internal conditions of the reinforcement learning algorithms. Experimental results revealed that the task could only be mastered using the method proposed PT-SEAC in this study, under high difficulty conditions that existing reinforcement learning algorithms struggled to achieve the task. These results suggest that cognitive functions may play an important role in complex observational learning in order to share the behavior of the other agent as their own experience.