Browsing by Author "Chiang, Han"

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Database Caching for Job-Level Computing
(2016-01-01)
Chiang, Han
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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
This paper improves upon Job-Level (JL) computing, a general distributed computing approach. In JL computing, a client maintains the overall search tree and parcels the overall search into coarse-grained jobs, which are then each calculated by pre-existing game-playing programs. In order to support large-scale problems such as solving 7x7 killall-Go, or building opening books for 9x9 Go or Connect6, JL computing is modified so that the entire search tree is stored in a database, as opposed to simply being stored in the client process' memory. However, the time cost of accessing this database becomes a bottleneck on performance when using a large number of computing resources. This paper proposes a cache mechanism for JL search trees. Instead of the previous approach, where the entire search tree is stored in the database, we maintain parts of the search tree in the memory of the client process to reduce the number of database accesses. Our method significantly improves the performance of job operations. Assuming that each job requires 30 seconds on average, the JL application with this cache mechanism can allow for the use of 5036 distributed computing resources in parallel without database accesses becoming the performance bottleneck.
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%.