An Approach to Robot Control using Deep Reinforcement Learning and Discretization by Words

Abstract

In recent years, the necessity for robots working in society has been spreading as the aging society has come. To easily be able to communicate with robots, it is expected that they can understand natural language and learn how to behave spontaneously through the interaction with humans. In this study, we aim to ground the meaning of natural language onto their behaviors by using reinforcement learning. In particular, we have proposed an efficient method to learn robot's motion with deep reinforcement learning by descritizing a robot's motion into a hierarchical structure consisting of basic motion elements which can be represented by words.