JSAI2018

Presentation information

Oral presentation

General Session » [General Session] 11. Robot / Real World

[2A3] [General Session] 11. Robot / Real World

Wed. Jun 6, 2018 3:20 PM - 5:00 PM Room A (4F Emerald Hall)

座長:稲邑 哲也(国立情報学研究所)

4:00 PM - 4:20 PM

[2A3-03] An Approach to Robot Control using Deep Reinforcement Learning and Discretization by Words

〇Sayuri Hashimoto1, Akira Kaneko1, Ichiro Kobayashi1 (1. Ochanomizu University)

Keywords:Deep Reinforcement Learning, Reinforcement Learning

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.