Approximate Model

Approximate models are simplified representations of complex systems used to improve efficiency and tractability in various machine learning and control problems. Current research focuses on developing methods to mitigate the negative impacts of model inaccuracies, including techniques like context-aware perturbations for explainability, maximum entropy model correction for reinforcement learning, and the incorporation of physics priors as regularizers in deep learning. These advancements are crucial for addressing challenges in high-dimensional systems and enabling the application of sophisticated algorithms to real-world problems where perfect models are unavailable, leading to improved performance and robustness in diverse fields like robotics, resource management, and scientific modeling.