To improve the simulated AL training for better generalization of the neural network policy to a wider range of real-world regression problems, several strategies can be employed:
Diverse Function Sampling: Increase the diversity of the functions sampled during the simulation phase. This can be achieved by using a broader set of kernel functions or by generating functions with varying degrees of complexity, including non-stationary and multi-modal functions. This diversity will help the policy learn to handle a wider array of function behaviors.
Adaptive Sampling Techniques: Implement adaptive sampling strategies that focus on regions of the function space where the model uncertainty is high. This can help the policy learn more effectively from challenging areas of the function space, improving its performance on real-world tasks.
Incorporation of Real Data: If available, integrating real-world data into the training process can significantly enhance the policy's ability to generalize. This could involve fine-tuning the policy on a small set of real observations after the initial training on simulated data.
Multi-Task Learning: Train the neural network policy using a multi-task learning framework, where the model learns from multiple related regression tasks simultaneously. This approach can help the model capture shared patterns across different tasks, leading to improved generalization.
Continuous Learning: Implement a continuous learning framework where the policy is periodically updated with new data as it becomes available. This allows the model to adapt to changes in the underlying function over time, ensuring that it remains effective in dynamic environments.
By employing these strategies, the simulated AL training can be made more robust, leading to a neural network policy that generalizes better across a variety of real-world regression problems.