Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search Article Swipe

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Li-Cheng Lan , Meng‐Yu Tsai , Ti-Rong Wu , I‐Chen Wu , Cho‐Jui Hsieh ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2012.07910

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. Also, under the same average simulation count, our method can achieve a 61% winning rate against the original program.

Related Topics

Monte Carlo Tree Search

Computer Science

Monte Carlo Method

Deep Learning

Artificial Intelligence

Mathematical Analysis

Concepts

Monte Carlo tree search Computer science Tree (set theory) Monte Carlo method Action (physics) State (computer science) Deep neural networks Artificial intelligence Speedup Artificial neural network Machine learning Algorithm Parallel computing Mathematics Statistics Quantum mechanics Physics Mathematical analysis

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2012.07910
PDF: https://arxiv.org/pdf/2012.07910
OA Status: green
Cited By: 1
References: 34
Related Works: 10
OpenAlex ID: https://openalex.org/W3110740179

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DOI: https://doi.org/10.48550/arxiv.2012.07910

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Title: Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-12-14

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Authors: Li-Cheng Lan, Meng‐Yu Tsai, Ti-Rong Wu, I‐Chen Wu, Cho‐Jui Hsieh

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Landing page: https://arxiv.org/abs/2012.07910

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Concepts: Monte Carlo tree search, Computer science, Tree (set theory), Monte Carlo method, Action (physics), State (computer science), Deep neural networks, Artificial intelligence, Speedup, Artificial neural network, Machine learning, Algorithm, Parallel computing, Mathematics, Statistics, Quantum mechanics, Physics, Mathematical analysis

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Cited by: 1

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abstract_inverted_index.earlier	113
abstract_inverted_index.implies	95
abstract_inverted_index.propose	127
abstract_inverted_index.remains	87
abstract_inverted_index.require	49
abstract_inverted_index.result.	122
abstract_inverted_index.results	8
abstract_inverted_index.similar	176
abstract_inverted_index.states,	80
abstract_inverted_index.trained	167
abstract_inverted_index.whether	147
abstract_inverted_index.winning	177, 192
abstract_inverted_index.However,	45
abstract_inverted_index.achieved	6
abstract_inverted_index.enormous	38
abstract_inverted_index.example,	65
abstract_inverted_index.identify	55
abstract_inverted_index.minutes.	93
abstract_inverted_index.networks	23
abstract_inverted_index.original	196
abstract_inverted_index.program.	197
abstract_inverted_index.requires	37
abstract_inverted_index.AlphaZero	169
abstract_inverted_index.combining	19
abstract_inverted_index.confident	117
abstract_inverted_index.executed,	29
abstract_inverted_index.predicted	84
abstract_inverted_index.resources	101
abstract_inverted_index.searching	52, 91, 112, 121, 139
abstract_inverted_index.unchanged	88
abstract_inverted_index.(DS-MCTS),	159
abstract_inverted_index.Simulation	157
abstract_inverted_index.algorithm,	154
abstract_inverted_index.predicting	133
abstract_inverted_index.resources.	44
abstract_inverted_index.searching.	151
abstract_inverted_index.simulation	184
abstract_inverted_index.maintaining	174
abstract_inverted_index.performance	34
abstract_inverted_index.significant	98
abstract_inverted_index.simulations	27
abstract_inverted_index.uncertainty	135
abstract_inverted_index.state-of-the-art	7
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile