Humanoid Robot Behavior Learning Based on ART Neural Network and Cross-Modality Learning

2006 ◽  
pp. 447-450 ◽  
Author(s):  
Lizhong Gu ◽  
Jianbo Su
Keyword(s):  
Neural Network ◽  
Humanoid Robot ◽  
Robot Behavior ◽  
Behavior Learning

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

2017 ◽  
Vol 21 (5) ◽  
pp. 840-848 ◽  
Author(s):  
Hikaru Sasaki ◽  
Tadashi Horiuchi ◽  
Satoru Kato ◽  
◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Visual Information ◽  
Profit Sharing ◽  
Simulation Software ◽  
Learning Performance ◽  
Simulation Environment ◽  
Q Learning ◽  
Robot Behavior ◽  
Target Network ◽  
Behavior Learning

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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