State Augmentation

State augmentation, a technique in machine learning and control systems, enhances model performance by incorporating additional information into the state representation. Current research focuses on applying this to various problems, including reinforcement learning (with algorithms like DDPG and variations of Monte Carlo methods), occupancy mapping, and image processing, often leveraging neural networks such as graph neural networks or convolutional neural networks with deformable convolutions. This approach addresses challenges like delayed observations, handling constraints, and improving robustness in dynamic or partially observable environments, leading to more efficient and effective algorithms across diverse applications.