Dynamical Pseudo-Random Number Generator Using Reinforcement Learning
Pseudo-random number generators (PRNGs) are based on the algorithm that generates a sequence of numbers arranged randomly. Recently, random numbers have been generated through a reinforcement learning mechanism. This method generates random numbers based on reinforcement learning characteristics tha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/7/3377 |
| _version_ | 1797440479515639808 |
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| author | Sungju Park Kyungmin Kim Keunjin Kim Choonsung Nam |
| author_facet | Sungju Park Kyungmin Kim Keunjin Kim Choonsung Nam |
| author_sort | Sungju Park |
| collection | DOAJ |
| description | Pseudo-random number generators (PRNGs) are based on the algorithm that generates a sequence of numbers arranged randomly. Recently, random numbers have been generated through a reinforcement learning mechanism. This method generates random numbers based on reinforcement learning characteristics that select the optimal behavior considering every possible status up to the point of episode closing to secure the randomness of such random numbers. The LSTM method is used for the long-term memory of previous patterns and selection of new patterns in consideration of such previous patterns. In addition, feature vectors extracted from the LSTM are accumulated, and their images are generated to overcome the limitation of LSTM long-term memory. From these generated images, features are extracted using CNN. This dynamical pseudo-random number generator secures the randomness of random numbers. |
| first_indexed | 2024-03-09T12:08:47Z |
| format | Article |
| id | doaj.art-8765f5df7c98453daa146543191950f4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:08:47Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8765f5df7c98453daa146543191950f42023-11-30T22:54:54ZengMDPI AGApplied Sciences2076-34172022-03-01127337710.3390/app12073377Dynamical Pseudo-Random Number Generator Using Reinforcement LearningSungju Park0Kyungmin Kim1Keunjin Kim2Choonsung Nam3Spiceware, 17F, 83, Uisadang-daero, Yeongdeungpo-gu, Seoul 07325, KoreaSpiceware, 17F, 83, Uisadang-daero, Yeongdeungpo-gu, Seoul 07325, KoreaSpiceware, 17F, 83, Uisadang-daero, Yeongdeungpo-gu, Seoul 07325, KoreaDepartment of Software Convergence Engineering, Inha University, Incheon 15798, KoreaPseudo-random number generators (PRNGs) are based on the algorithm that generates a sequence of numbers arranged randomly. Recently, random numbers have been generated through a reinforcement learning mechanism. This method generates random numbers based on reinforcement learning characteristics that select the optimal behavior considering every possible status up to the point of episode closing to secure the randomness of such random numbers. The LSTM method is used for the long-term memory of previous patterns and selection of new patterns in consideration of such previous patterns. In addition, feature vectors extracted from the LSTM are accumulated, and their images are generated to overcome the limitation of LSTM long-term memory. From these generated images, features are extracted using CNN. This dynamical pseudo-random number generator secures the randomness of random numbers.https://www.mdpi.com/2076-3417/12/7/3377reinforcement learningdynamical pseudo-random number generatorRNNCNNagentenvironment |
| spellingShingle | Sungju Park Kyungmin Kim Keunjin Kim Choonsung Nam Dynamical Pseudo-Random Number Generator Using Reinforcement Learning Applied Sciences reinforcement learning dynamical pseudo-random number generator RNN CNN agent environment |
| title | Dynamical Pseudo-Random Number Generator Using Reinforcement Learning |
| title_full | Dynamical Pseudo-Random Number Generator Using Reinforcement Learning |
| title_fullStr | Dynamical Pseudo-Random Number Generator Using Reinforcement Learning |
| title_full_unstemmed | Dynamical Pseudo-Random Number Generator Using Reinforcement Learning |
| title_short | Dynamical Pseudo-Random Number Generator Using Reinforcement Learning |
| title_sort | dynamical pseudo random number generator using reinforcement learning |
| topic | reinforcement learning dynamical pseudo-random number generator RNN CNN agent environment |
| url | https://www.mdpi.com/2076-3417/12/7/3377 |
| work_keys_str_mv | AT sungjupark dynamicalpseudorandomnumbergeneratorusingreinforcementlearning AT kyungminkim dynamicalpseudorandomnumbergeneratorusingreinforcementlearning AT keunjinkim dynamicalpseudorandomnumbergeneratorusingreinforcementlearning AT choonsungnam dynamicalpseudorandomnumbergeneratorusingreinforcementlearning |