Accelerating and Improving AlphaZero Using Population Based Training Article Swipe
Ti-Rong Wu
,
Ting-Han Wei
,
I‐Chen Wu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i01.5454
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v34i01.5454
AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations. This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another significant advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19x19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v34i01.5454
- https://ojs.aaai.org/index.php/AAAI/article/download/5454/5310
- OA Status
- diamond
- Cited By
- 15
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2997184839
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2997184839Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v34i01.5454Digital Object Identifier
- Title
-
Accelerating and Improving AlphaZero Using Population Based TrainingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-04-03Full publication date if available
- Authors
-
Ti-Rong Wu, Ting-Han Wei, I‐Chen WuList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v34i01.5454Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/5454/5310Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/5454/5310Direct OA link when available
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Hyperparameter, Computer science, Training (meteorology), Population, Hyperparameter optimization, Machine learning, Artificial intelligence, Simulation, Sociology, Support vector machine, Meteorology, Demography, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 5, 2023: 3, 2022: 3, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.amount | 14 |
| abstract_inverted_index.during | 44, 81 |
| abstract_inverted_index.games. | 7 |
| abstract_inverted_index.higher | 138 |
| abstract_inverted_index.method | 90, 132 |
| abstract_inverted_index.needed | 60 |
| abstract_inverted_index.neural | 191 |
| abstract_inverted_index.obtain | 165, 175 |
| abstract_inverted_index.single | 93 |
| abstract_inverted_index.though | 112 |
| abstract_inverted_index.tuning | 27 |
| abstract_inverted_index.Another | 84 |
| abstract_inverted_index.OpenGo, | 183 |
| abstract_inverted_index.achieve | 136 |
| abstract_inverted_index.against | 181, 212 |
| abstract_inverted_index.improve | 79 |
| abstract_inverted_index.network | 192 |
| abstract_inverted_index.non-PBT | 203 |
| abstract_inverted_index.playing | 168 |
| abstract_inverted_index.program | 188 |
| abstract_inverted_index.records | 109 |
| abstract_inverted_index.remains | 110 |
| abstract_inverted_index.result, | 55 |
| abstract_inverted_index.trained | 154 |
| abstract_inverted_index.usually | 59 |
| abstract_inverted_index.achieves | 206 |
| abstract_inverted_index.compared | 199 |
| abstract_inverted_index.consumes | 11 |
| abstract_inverted_index.generate | 48 |
| abstract_inverted_index.majority | 19 |
| abstract_inverted_index.multiple | 56 |
| abstract_inverted_index.proposes | 67 |
| abstract_inverted_index.records. | 52 |
| abstract_inverted_index.requires | 36, 91 |
| abstract_inverted_index.strength | 80 |
| abstract_inverted_index.training | 30, 71, 82, 122 |
| abstract_inverted_index.AlphaZero | 0, 121, 187 |
| abstract_inverted_index.advantage | 86 |
| abstract_inverted_index.capacity. | 196 |
| abstract_inverted_index.computing | 16 |
| abstract_inverted_index.different | 62 |
| abstract_inverted_index.following | 119 |
| abstract_inverted_index.increased | 118 |
| abstract_inverted_index.incurring | 97 |
| abstract_inverted_index.saturated | 202 |
| abstract_inverted_index.self-play | 51, 108 |
| abstract_inverted_index.strength. | 169 |
| abstract_inverted_index.unchanged | 111 |
| abstract_inverted_index.additional | 100 |
| abstract_inverted_index.algorithm. | 123 |
| abstract_inverted_index.baselines, | 146 |
| abstract_inverted_index.comparable | 195 |
| abstract_inverted_index.generating | 107 |
| abstract_inverted_index.population | 69 |
| abstract_inverted_index.resources, | 17 |
| abstract_inverted_index.self-play. | 25 |
| abstract_inverted_index.successful | 4 |
| abstract_inverted_index.dynamically | 77 |
| abstract_inverted_index.exacerbates | 28 |
| abstract_inverted_index.experiments | 126 |
| abstract_inverted_index.open-source | 185 |
| abstract_inverted_index.significant | 85 |
| abstract_inverted_index.improvements | 166 |
| abstract_inverted_index.optimization | 116 |
| abstract_inverted_index.Specifically, | 170 |
| abstract_inverted_index.configuration | 35, 152 |
| abstract_inverted_index.individually. | 155 |
| abstract_inverted_index.Hyperparameter | 26 |
| abstract_inverted_index.Unfortunately, | 8 |
| abstract_inverted_index.circumstances. | 218 |
| abstract_inverted_index.hyperparameter | 34, 63, 151 |
| abstract_inverted_index.configurations. | 64 |
| abstract_inverted_index.hyperparameters | 76 |
| abstract_inverted_index.state-of-the-art | 186 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.7947494 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |