Transition Model

Transition models are mathematical representations of how systems change over time, focusing on predicting future states based on current states and actions. Current research emphasizes learning these models from data, employing techniques like neural hidden Markov models, adversarial networks, and graph neural networks to improve accuracy and robustness, particularly in complex, high-dimensional systems. These advancements are crucial for applications ranging from reinforcement learning and robotics control to healthcare prediction and time series analysis, enabling more effective decision-making and improved system performance in diverse fields.