Graph Based Reinforcement Learning

Description

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.