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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 1: AAAI-21 Technical Tracks 1

Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search

February 1, 2023

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Authors

Li-Cheng Lan

Department of Computer Science, UCLA


Ti-Rong Wu

Department of Computer Science, National Chiao-Tung University, Taiwan


I-Chen Wu

Department of Computer Science, National Chiao-Tung University, Taiwan Research Center for IT Innovation, Academia Sinica, Taiwan


Cho-Jui Hsieh

Department of Computer Science, UCLA


DOI:

10.1609/aaai.v35i1.16100


Abstract:

Monte Carlo tree search (MCTS) has achieved state-of-the-art results in many domains such as Go and Atari games when combining with deep neural networks (DNNs). When more simulations are executed, MCTS can achieve higher performance but also requires enormous amounts of CPU and GPU resources. However, not all states require a long searching time to identify the best action that the agent can find. For example, in 19x19 Go and NoGo, we found that for more than half of the states, the best action predicted by DNN remains unchanged even after searching 2 minutes. This implies that a significant amount of resources can be saved if we are able to stop the searching earlier when we are confident with the current searching result. In this paper, we propose to achieve this goal by predicting the uncertainty of the current searching status and use the result to decide whether we should stop searching. With our algorithm, called Dynamic Simulation MCTS (DS-MCTS), we can speed up a NoGo agent trained by AlphaZero 2.5 times faster while maintaining a similar winning rate, which is critical for training and conducting experiments. Also, under the same average simulation count, our method can achieve a 61% winning rate against the original program.

Topics: AAAI

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HOW TO CITE:

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search Proceedings of the AAAI Conference on Artificial Intelligence (2021) 259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search AAAI 2021, 259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh (2021). Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. Proceedings of the AAAI Conference on Artificial Intelligence, 259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh. Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh. 2021. Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. "Proceedings of the AAAI Conference on Artificial Intelligence". 259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh. (2021) "Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search", Proceedings of the AAAI Conference on Artificial Intelligence, p.259-267

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh, "Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search", AAAI, p.259-267, 2021.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh. "Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh. "Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 259-267.

Li-Cheng Lan||Ti-Rong Wu||I-Chen Wu||Cho-Jui Hsieh. Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. AAAI[Internet]. 2021[cited 2023]; 259-267.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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