“Graph Based Reinforcement Learning” Project

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

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