Non Markovian

Non-Markovian systems defy the simplifying assumption that future states depend only on the present, requiring consideration of past history. Current research focuses on developing efficient algorithms and models, such as recurrent neural networks, automata-based frameworks (including neural reward machines), and novel approaches like Neural Integro-Differential Equations, to handle this complexity in various applications, including reinforcement learning and quantum process learning. These advancements are crucial for accurately modeling and controlling real-world systems exhibiting temporal dependencies, improving the performance of AI agents and enabling more realistic simulations in diverse fields like healthcare, climate science, and neuroscience.