# Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games
## 1 Introduction  
Natural language processing (NLP), a core area of artificial intelligence (AI), aims to enable machines to understand, interpret, and generate human language. With the rise of intelligent systems, NLP techniques have been widely applied in diverse fields such as text mining, machine translation, and sentiment analysis. Recently, the increasing popularity of interactive word games like **Wordle** has introduced a novel and engaging scenario for studying language-based user behavior models in dynamic and gamification environments. In **Wordle**, players attempt to guess a hidden five-letter word within six tries, receiving structured feedback after each guess. Beyond simple vocabulary recall, the game requires logical reasoning and decision-making under uncertainty. Moreover, **Wordle** publicly releases daily statistics on the number of attempts users need for different words. These data offer rich linguistic and behavioral signals such as word frequency, letter occurrence, and repetition patterns. which provide a valuable foundation for modeling user guessing behaviors. However, traditional machine learning methods face several challenges when modeling this problem. Although Random Forest Regression (RFR) [1] has been widely used for feature importance analysis, its complex ensemble structure often leads to overfitting, especially in noisy or high-dimensional datasets. Moreover, the prediction speed of RFR is limited due to the need to traverse multiple decision trees [2]. XGBoost [3] improves the limitations of RFR by constructing weak learners in sequence and introducing regularization to prevent overfitting. However, it requires the manual encoding of categorical features and may struggle to adapt quickly to dynamic changes in the game environment [4]. LightGBM [5] further optimizes model efficiency by adopting a leaf-wise growth strategy and supporting the native processing of categorical features. Nevertheless, it remains sensitive to noisy data and outliers, which affects its robustness [6]. DDPG [7] introduces deterministic policy gradients, avoiding the costly sampling of policy distributions in continuous-action spaces by directly updating policy parameters via gradients, which enables it to excel on high-dimensional continuous-control problems. It combines a deep Q-network with a deterministic policy network using a replay buffer and target network mechanisms, ensuring stable training while improving sample efficiency [8; 9; 10]. While deep learning has achieved remarkable success in various NLP tasks, it may not be ideally suited for this problem. First, the **Wordle** dataset is relatively small and consists of low-dimensional, well-structured linguistic features, which limits the effectiveness of deep models and increases the risk of overfitting. Second, the \"black-box\" nature of deep neural networks makes it difficult to explain and trust their predictions--an essential requirement when modeling human decision behavior. Third, most deep learning models are designed for static inference tasks and lack the inherent ability to interact with dynamic feedback environments like **Wordle**. These limitations further highlight the need for a method that is both adaptive and interpretable. To address the aforementioned challenges and leverage the strengths of the original DDPG, this study proposes an integrated framework combining reinforcement learning (RL) and interpretable artificial intelligence (I-AI). Specifically, we propose an Enhanced-DDPG algorithm that dynamically predicts the distribution of **Wordle** attempt counts and incorporates SHapley Additive exPlanations (SHAP) to interpret the model's internal decision-making process. By using key linguistic features such as word frequency, letter frequency, and repetition patterns, our approach demonstrates superior prediction accuracy and robustness compared to baseline models such as Random Forest, XGBoost, and LightGBM.