To further improve performance on limited data, CPL could benefit from the incorporation of additional conservative regularizers that encourage stability, generalization, and robustness in the learning process. Some potential types of conservative regularizers that could be integrated into the CPL objective include:
Entropy Regularization: By adding an entropy term to the loss function, CPL can be encouraged to explore a more diverse set of actions and policies, leading to better generalization and improved performance on limited data. Entropy regularization helps prevent the model from becoming overly confident in its predictions and promotes exploration of different strategies.
L1 or L2 Regularization: Including L1 or L2 regularization terms in the objective function can help prevent overfitting and promote sparsity in the learned policy. Regularizing the model parameters can lead to more stable and generalizable policies, especially in scenarios with limited data where overfitting is a concern.
Diversity Regularization: Introducing a diversity regularizer that penalizes policies that are too similar to each other can encourage the model to explore a wider range of behaviors. By promoting diversity in the learned policies, CPL can capture a more comprehensive understanding of the task and make better decisions in diverse scenarios.
Adversarial Regularization: Incorporating adversarial training techniques into the CPL framework can help improve the robustness of the learned policies. By training the model against adversarial examples or opponents, CPL can learn to make decisions that are more resilient to perturbations and uncertainties in the environment.
By integrating these types of conservative regularizers into the CPL objective, the model can enhance its performance on limited data by promoting stability, generalization, and robustness in policy learning.