PI: Laura Toni
PhD Student: Sephora Madjiheurem
Using graph signal processing to improve the data-efficiency of reinforcement learning algorithms
Classical reinforcement learning problems suffer from the well-known curse of dimensionality, leading to very slow learning problem in high-dimensional search spaces.
In this project, we aim to overcome this limitation by inferring the structure/geometry of the problem in the learning algorithm in the case of high-dimensional states.
Possible applications are online control of grid networks or traffic networks