The proposed coarse-to-fine approach can be extended to handle even longer prediction horizons or a larger number of agents by implementing a few key strategies:
Hierarchical Prediction: Instead of directly forecasting the final outcome, the prediction can be broken down into multiple hierarchical levels. Each level can focus on a different timescale or group of agents, allowing for more granular and accurate predictions.
Progressive Refinement: The coarse predictions can serve as initial estimates for finer predictions. By iteratively refining the predictions based on feedback loops or additional context, the model can improve the accuracy of long-term forecasts.
Attention Mechanisms: Implementing attention mechanisms that dynamically adjust the focus on different agents or time steps can help the model adapt to longer horizons or larger agent groups. This way, the model can prioritize relevant information for each prediction.
Memory Networks: Introducing memory networks can enable the model to store and retrieve relevant information from past interactions, aiding in making more informed predictions over extended periods or with more agents involved.