Stochastic Network
Stochastic networks model systems with probabilistic dynamics, focusing on optimizing performance under uncertainty in areas like network scheduling and path planning. Current research emphasizes developing algorithms that handle non-stationary conditions and limited feedback, often employing reinforcement learning techniques, Lyapunov analysis, and novel gradient estimation methods within frameworks like Markov decision processes and graph neural networks. These advancements improve the robustness and efficiency of decision-making in complex systems with applications ranging from multi-agent coordination to medical image analysis and epidemic modeling. The ultimate goal is to create more reliable and adaptable systems capable of operating effectively in unpredictable environments.