Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task.

We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning.

Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success.