“Re:ROS”: Prototyping of Reinforcement Learning Environment for Asynchronous Cognitive Architecture

Abstract

Reinforcement learning (RL), which is a field of machine learning, is effective for behavior acquisition in robots. Asynchronous cognitive architecture, which is a method to model human intelligence, is also effective for behavior acquisition. Accordingly, the combination of RL and asynchronous cognitive architecture is expected to be effective. However, early work on the RL toolkit cannot apply asynchronous cognitive architecture because it cannot solve the difference between the asynchrony, which the asynchronous cognitive architecture has, and the synchrony, which RL modules have. In this study, we propose an RL environment for robots that can apply the asynchronous cognitive architecture by applying asynchronous systems to RL modules. We prototyped the RL environment named “Re:ROS.”.