Transition Operator

Transition operators, fundamental to modeling dynamical systems and sequential decision-making, describe the probability of transitioning between states in a system. Current research focuses on efficiently learning these operators from limited or noisy data, employing techniques like neural network parameterizations and leveraging inherent system structure (e.g., exploiting noise properties) to improve accuracy and efficiency. These advancements are crucial for improving reinforcement learning algorithms and enabling the analysis of complex systems where complete knowledge is unavailable, impacting fields ranging from control theory to machine learning.