Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games
Accurate and interpretable prediction plays a vital role in natural language processing (NLP) tasks, particularly for enhancing user trust and model transparency. However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction t...
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MDPI AG
2025-07-01
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| author | Jun Zhao Jintian Ji Robail Yasrab Shuxin Wang Liang Yu Lingzhen Zhao |
| author_facet | Jun Zhao Jintian Ji Robail Yasrab Shuxin Wang Liang Yu Lingzhen Zhao |
| author_sort | Jun Zhao |
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| description | Accurate and interpretable prediction plays a vital role in natural language processing (NLP) tasks, particularly for enhancing user trust and model transparency. However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction tasks such as <b>Wordle</b>. To address these challenges, this study proposes an interpretable reinforcement learning framework based on an Enhanced Deep Deterministic Policy Gradient (Enhanced-DDPG) algorithm. By leveraging a custom simulation environment and integrating key linguistic features word frequency, letter frequency, and repeated letter patterns (rep) the model dynamically predicts the number of attempts needed to solve <b>Wordle</b> puzzles. Experimental results demonstrate that Enhanced-DDPG outperforms traditional methods such as Random Forest Regression (RFR), XGBoost, LightGBM, METRA, and SQIRL in terms of both prediction accuracy (MSE = 0.0134, R<sup>2</sup> = 0.8439) and robustness under noisy conditions. Furthermore, SHapley Additive exPlanations (SHAP) are employed to interpret the model’s decision process, revealing that repeated letter patterns significantly influence low-attempt predictions, while word and letter frequencies are more relevant for higher attempt scenarios. This research highlights the potential of combining interpretable artificial intelligence (I-AI) and reinforcement learning to develop robust, transparent, and high-performance NLP prediction systems for real-world applications. |
| format | Article |
| id | doaj-art-5aa4f781eb9544e2b333e882fd95f04a |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-5aa4f781eb9544e2b333e882fd95f04a2025-08-20T02:45:52ZengMDPI AGAlgorithms1999-48932025-07-0118742710.3390/a18070427Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based GamesJun Zhao0Jintian Ji1Robail Yasrab2Shuxin Wang3Liang Yu4Lingzhen Zhao5Department of Public Foundation, Wannan Medical College, Wuhu 241000, ChinaDepartment of Medical Information, Wannan Medical College, Wuhu 241000, ChinaMRC Biostatistics Unit, University of Cambridge, Cambridge CB2 1TN, UKDepartment of Medical Information, Wannan Medical College, Wuhu 241000, ChinaDepartment of Public Foundation, Wannan Medical College, Wuhu 241000, ChinaDepartment of Public Foundation, Wannan Medical College, Wuhu 241000, ChinaAccurate and interpretable prediction plays a vital role in natural language processing (NLP) tasks, particularly for enhancing user trust and model transparency. However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction tasks such as <b>Wordle</b>. To address these challenges, this study proposes an interpretable reinforcement learning framework based on an Enhanced Deep Deterministic Policy Gradient (Enhanced-DDPG) algorithm. By leveraging a custom simulation environment and integrating key linguistic features word frequency, letter frequency, and repeated letter patterns (rep) the model dynamically predicts the number of attempts needed to solve <b>Wordle</b> puzzles. Experimental results demonstrate that Enhanced-DDPG outperforms traditional methods such as Random Forest Regression (RFR), XGBoost, LightGBM, METRA, and SQIRL in terms of both prediction accuracy (MSE = 0.0134, R<sup>2</sup> = 0.8439) and robustness under noisy conditions. Furthermore, SHapley Additive exPlanations (SHAP) are employed to interpret the model’s decision process, revealing that repeated letter patterns significantly influence low-attempt predictions, while word and letter frequencies are more relevant for higher attempt scenarios. This research highlights the potential of combining interpretable artificial intelligence (I-AI) and reinforcement learning to develop robust, transparent, and high-performance NLP prediction systems for real-world applications.https://www.mdpi.com/1999-4893/18/7/427interpretable artificial intelligencereinforcement learningnatural language processingenhanced deep deterministic policy gradient<b>Wordle</b> prediction |
| spellingShingle | Jun Zhao Jintian Ji Robail Yasrab Shuxin Wang Liang Yu Lingzhen Zhao Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games Algorithms interpretable artificial intelligence reinforcement learning natural language processing enhanced deep deterministic policy gradient <b>Wordle</b> prediction |
| title | Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games |
| title_full | Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games |
| title_fullStr | Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games |
| title_full_unstemmed | Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games |
| title_short | Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games |
| title_sort | interpretable reinforcement learning for sequential strategy prediction in language based games |
| topic | interpretable artificial intelligence reinforcement learning natural language processing enhanced deep deterministic policy gradient <b>Wordle</b> prediction |
| url | https://www.mdpi.com/1999-4893/18/7/427 |
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