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JACIII Vol.21 No.5 pp. 840-848
doi: 10.20965/jaciii.2017.p0840
(2017)

Paper:

Experimental Study on Behavior Acquisition of Mobile Robot by Deep Q-Network

Hikaru Sasaki*, Tadashi Horiuchi**, and Satoru Kato***

*Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan

**Department of Control Engineering, National Institute of Technology, Matsue College
14-4 Nishi-ikuma, Matsue, Shimane 690-8518, Japan

***Department of Information Engineering, National Institute of Technology, Matsue College
14-4 Nishi-ikuma, Matsue, Shimane 690-8518, Japan

Received:
March 21, 2017
Accepted:
July 21, 2017
Published:
September 20, 2017
Keywords:
deep reinforcement learning, deep Q-network, mobile robot, behavior acquisition
Abstract

Deep Q-network (DQN) is one of the most famous methods of deep reinforcement learning. DQN approximates the action-value function using Convolutional Neural Network (CNN) and updates it using Q-learning. In this study, we applied DQN to robot behavior learning in a simulation environment. We constructed the simulation environment for a two-wheeled mobile robot using the robot simulation software, Webots. The mobile robot acquired good behavior such as avoiding walls and moving along a center line by learning from high-dimensional visual information supplied as input data. We propose a method that reuses the best target network so far when the learning performance suddenly falls. Moreover, we incorporate Profit Sharing method into DQN in order to accelerate learning. Through the simulation experiment, we confirmed that our method is effective.

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Last updated on Oct. 20, 2017