# Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games
## 5 Results
### Interpretability Analysis  
In the field of deep learning, model interpretability has always been the focus of research on improving model transparency. With the increase in model complexity, how to interpret the prediction results is crucial. As an explanation method based on game theory, SHAP provides a unified explanation framework for the prediction results of deep learning models. The core idea of SHAP is derived from the Shapley value in game theory, which is used to measure the average contribution of a player in a cooperative game. In the displayed shapes, each feature is regarded as a player, and the predictions of a model are the result of the game, calculating the marginal contribution of each feature. Among all possible feature subsets, the SHAP value is a value assigned to each feature, indicating the contribution of this feature to the prediction result. For this study, the calculation process is as follows:  
Step 1: For each sample, generate a sample with a slightly different feature value from the original sample, which is called a perturbed sample. Step 2: Use the model to predict the prediction results of the original sample and the perturbed sample. Step 3: Calculate the difference between the prediction results of the perturbed sample and the prediction results of the original sample, which is the SHAP value of the feature. Step 4: Obtain the SHAP value of each feature by a weighted average of all possible feature subsets. Finally, we obtain the detailed results, as shown in Figure 6, where the horizontal axis (X-axis) is the SHAP value (i.e., the magnitude and direction of each feature's influence on the model output). The vertical axis (Y-axis) is the feature name (rep, letter frequency, word frequency). The color indicates the size of the feature value, with the color bar on the right. Red indicates a high feature value and blue indicates a low feature value. Figure 5: Robustness analysis results of DDPG algorithm. This scatter plot compares actual values with predicted values from DDPG algorithm. Blue dots represent predictions made on the original dataset, and red dots represent the predict result based on Gaussian noise data, showing the algorithm’s prediction accuracy and robustness by illustrating the algorithm performance and maintaining stability despite the presence of noise in the data. The following is a detail analysis of each subplot within the SHAP summary plot shown in Figure 6.  
In the one-try case, rep features display concentrated, positive SHAP values. This suggests that in one-try classification, high-rep features tend to guide the model to predict that class. Letter frequency and word frequency have minimal influence on the model, with SHAP values near zero, indicating that these features have little impact on the judgment of whether it is \"one try.\"  
In the two-tries case, the rep continues to have the strongest impact, positively influencing the model output. Word frequency and letter frequency have some negative effects on the model, but these effects are still relatively small. Note that the SHAP values are more widely distributed here, implying that the feature influence is somewhat more complex. In the three-tries case, word frequency has the largest impact, both positive and negative, with SHAP values ranging from \(-4\) to \(+3\). The SHAP values of letter frequency are more densely clustered around zero but still show some positive and negative effects. Figure 6: Analysis of SHAP interpretability. The rep feature also has an effect, though it is slightly weaker than before, with less scatter concentration. In the four-tries case, the SHAP values for all three features are relatively balanced, indicating that the model relies on multiple features when judging \"four tries.\" The lack of dominant features suggests that this class is more complex or more evenly distributed in the training data. In the five-tries case, the SHAP values for rep are highly concentrated but negative in direction, indicating that low values push down the model output.