Transition Dynamic

Transition dynamics research focuses on modeling and predicting how systems change state over time, with applications ranging from pandemic modeling to robotics and AI. Current research emphasizes learning these dynamics from data, employing diverse approaches including recurrent neural networks (like rSLDS), Markov models, and physics-informed neural networks, often within reinforcement learning or imitation learning frameworks. These advancements aim to improve the accuracy and efficiency of predictions, enabling better decision-making in complex systems and facilitating the development of more robust and adaptable AI agents. The ultimate goal is to create more reliable and efficient systems capable of handling uncertainty and adapting to changing environments.