Significant Delay Change

Significant delay change research focuses on understanding and mitigating the negative impacts of delayed information in various dynamic systems. Current research explores this through diverse approaches, including actor-critic algorithms for robust decision-making in autonomous vehicles, stream processing for real-time delay detection in public transport, and reinforcement learning methods that account for delayed feedback in control problems and online decision-making. These advancements are crucial for improving the efficiency and robustness of systems ranging from autonomous driving and public transportation to resource-constrained healthcare diagnostics and large-scale machine learning.