Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games

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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Bibliographic Details
Main Authors: Jun Zhao, Jintian Ji, Robail Yasrab, Shuxin Wang, Liang Yu, Lingzhen Zhao
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Algorithms
Subjects:
interpretable artificial intelligence
reinforcement learning
natural language processing
enhanced deep deterministic policy gradient
<b>Wordle</b> prediction
Online Access:https://www.mdpi.com/1999-4893/18/7/427
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