One potential limitation of the amortized AL method is its reliance on the quality of the initial training data and the simulated functions used for policy training. If the simulated functions do not adequately represent the complexity of real-world functions, the neural network policy may perform poorly when deployed. To address this, one could enhance the diversity of the simulated function space by incorporating a wider range of function types and noise levels during the training phase, ensuring that the policy is robust to various scenarios.
Another drawback is the potential for overfitting the neural network policy to the simulated data, which may not translate well to real-world applications. Regularization techniques, such as dropout or weight decay, can be employed during training to mitigate overfitting. Additionally, implementing a validation phase where the policy is tested on unseen simulated functions can help assess its generalization capabilities before deployment.
Furthermore, the computational efficiency of the amortized AL method may be compromised if the neural network becomes too complex, leading to longer inference times. To address this, one could explore model compression techniques, such as pruning or quantization, to reduce the model size and improve inference speed without significantly sacrificing performance.