Browsing by Author "Lee, Chin-Ling"

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Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games
(2019-10-01)
Lin, Cheng-Jian
;
Jhang, Jyun-Yu
;
Lin, Hsueh-Yi
;
Lee, Chin-Ling
;
Young, Kuu-Young
This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the video game. Both Flappy Bird and Atari Breakout games were implemented to verify the proposed method in this study. Experimental results showed that the scores of our proposed RQDNN were better than those of human players and other methods. In addition, the training time of the proposed RQDNN was also far less than other methods.
Using a Self-Clustering Algorithm and Type-2 Fuzzy Controller for Multi-robot Deployment and Navigation in Dynamic Environments
(1970-01-01)
Jhang, Jyun-Yu
;
Lee, Chin-Ling
;
Lin, Cheng-Jian
;
Young, Kuu-Young
;
電控工程研究所
This study proposes a novel method for multi-robot deployment and navigation under dynamic environments. To automatically determine the location deployment of multiple robots, a grid-based method and self-clustering algorithm (SCA) were used to simplify the environmental information and automatically deploy robot locations. In the navigation process, a behavior selector automatically turns on towards goal mode or wall-following mode (WFM) depending on environmental conditions. WFM control adopts an interval type-2 fuzzy controller (IT2FC). The parameters of the IT2FC are adjusted by using the dynamic group whale optimization algorithm (DGWOA). The proposed DGWOA uses a dynamic group and Levy flight strategy to overcome the problem of falling into a local minimum solution. Experimental results reveal that the proposed method can successfully complete navigation tasks under dynamic environments.