Browsing by Author "Guei, Hung"

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2048-like games for teaching reinforcement learning
(2020-01-01)
Guei, Hung
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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
2048-like games are a family of single-player stochastic puzzle games, which consist of sliding numbered-tiles that combine to form tiles with larger numbers. Notable examples of games in this family include Threes!, 2048, and 2584. 2048-like games are highly suitable for educational purposes due to their simplicity and popularity. Numerous machine learning methods have been proposed to play 2048-like games; the application of these techniques can help students gain first-hand experience in implementing machine learning algorithms. This paper proposes a guideline for using 2048-like games for teaching reinforcement learning and computer game algorithms, while also summarizing our experience of using 2584 and Threes! as pedagogical tools that were well received judging by student feedback in two graduate level courses.
On Strength Adjustment for MCTS-Based Programs
(2019-01-01)
Wu, I-Chen
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Wu, Ti-Rong
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Liu, An-Jen
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Guei, Hung
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Wei, Tinghan
This paper proposes an approach to strength adjustment for MCTS-based game-playing programs. In this approach, we use a softmax policy with a strength index to choose moves. Most importantly, we filter low quality moves by excluding those that have a lower simulation count than a pre-defined threshold ratio of the maximum simulation count. We perform a theoretical analysis, reaching the result that the adjusted policy is guaranteed to choose moves exceeding a lower bound in strength by using a threshold ratio. The approach is applied to the Go program ELF OpenGo. The experiment results show that is highly correlated to the empirical strength; namely, given a threshold ratio 0.1, z is linearly related to the Elo rating with regression error 47.95 Elo where -2 <= z <= 2. Meanwhile, the covered strength range is about 800 Elo ratings in the interval of in. With the ease of strength adjustment using, we present two methods to adjust strength and predict opponents' strengths dynamically. To our knowledge, this result is state-of-the-art in terms of the range of strengths in Elo rating while maintaining a controllable relationship between the strength and a strength index.
Strength Adjustment and Assessment for MCTS-Based Programs [Research Frontier]
(2020-08-01)
Liu, An-Jen
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Wu, Ti-Rong
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Wu, I-Chen
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Guei, Hung
;
Wei, Ting-Han
This paper proposes an approach to strength adjustment and assessment for Monte-Carlo tree search based game-playing programs. We modify an existing softmax policy with a strength index to choose moves. The most important modification is a mechanism which filters low-quality moves by excluding those that have a lower simulation count than a pre-defined threshold ratio of the maximum simulation count. Through theoretical analysis, we show that the adjusted policy is guaranteed to choose moves exceeding a lower bound in strength by using a threshold ratio. Experimental results show that the strength index is highly correlated to the empirical strength. With an index value between ?2, we can cover a strength range of about 800 Elo ratings. The strength adjustment and assessment methods were also tested in real-world scenarios with human players, ranging from professionals (strongest) to kyu rank amateurs (weakest). For amateur levels, we tested our mechanism on two popular Go online platforms - Fox Weiqi and Tygem. The result shows that our method can adjust program strength to different ranks stably. In terms of strength assessment, we proposed a new dynamic strength adjustment method, then used it to evaluate human professionals, predicting reliably their playing strengths within 15 games. Lastly, we collected survey responses asking players about strength perception, entertainment, and general comments for different aspects of analysis. To our best knowledge, this result is state-ofthe- art in terms of the range of strengths in Elo rating while maintaining a controllable relationship between the strength and a strength index.
Using 2048-like games as a pedagogical tool for reinforcement learning
(2018-01-01)
Guei, Hung
;
Wei, Ting-Han
;
Wu, I-Chen
;
資訊工程學系
;
Department of Computer Science
2048-like games are games that have similar properties with 2048, a single-player stochastic sliding puzzle game. 2048-like games are highly suitable for educational purposes due to 2048's relatively simple rules and its popularity. When using 2048-like games as a tool for machine learning education, these games have the additional benefit of being a well-known topic of research. Numerous machine learning methods have been proposed in the past for 2048, which provides a good opportunity for students to gain first-hand experience in applying these techniques. This paper summarizes the experience of using the game 2584, a 2048-like game, as a pedagogical tool for teaching reinforcement learning and computer game algorithms in 2017. 2584 is similar to 2048, with the only difference being the tiles values are Fibonacci numbers instead of powers of two. A two-player variant was designed to further teach adversarial game techniques. With a class of 33 undergraduate and graduate students, the average win rate for the single-player version of the 2584 reached 96%.