Browsing by Author "Chang, Chia-Chuan"

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Job-Level Computing With BOINC Support
(2016-01-01)
Chang, Chia-Chuan
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Wei, Ting-Han
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Wu, I-Chen
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資訊工程學系
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Department of Computer Science
Recently, Wu et al. introduced a general approach based on distributed computing named Job-Level (JL) Computing. JL Computing has been successfully used to construct the opening books of game-playing programs. In order to support large-scale computing problems such as solving 7x7 killall-Go, or building opening books for 9x9 Go or Connect6, record databases are used to store JL computing results. In this paper, we further design a mechanism that combines the JL computing system with BOINC (Berkeley Open Infrastructure for Network Computing), so that we can leverage more computing power from volunteers to solve even larger problems. A preliminary experiment has been done to demonstrate the feasibility of the design.
Multistage Temporal Difference Learning for 2048-Like Games
(2017-12-01)
Yeh, Kun-Hao
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Wu, I-Chen
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Hsueh, Chu-Hsuan
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Chang, Chia-Chuan
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Liang, Chao-Chin
Szubert and Jaskowski successfully used temporal difference (TD) learning together with n-tuple networks for playing the game 2048. However, we observed a phenomenon that the programs based on TD learning still hardly reach large tiles. In this paper, we propose multistage TD (MS-TD) learning, a kind of hierarchical reinforcement learning method, to effectively improve the performance for the rates of reaching large tiles, which are good metrics to analyze the strength of 2048 programs. Our experiments showed significant improvements over the one without using MS-TD learning. Namely, using 3-ply expectimax search, the program with MS-TD learning reached 32768-tiles with a rate of 18.31%, while the one with TD learning did not reach any. After further tuned, our 2048 program reached 32768-tiles with a rate of 31.75% in 10,000 games, and one among these games even reached a 65536-tiles, which is the first ever reaching a 65536-tiles to our knowledge. In addition, MS-TD learning method can be easily applied to other 2048-like games, such as Threes. Based on MS-TD learning, our experiments for Threes also demonstrated similar performance improvement, where the program with MS-TD learning reached 6144-tiles with a rate of 7.83%, while the one with TD learning only reached 0.45%.