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Publication
CDC 2019
Conference paper
Reinforcement Learning Augmented Optimization for Smart Mobility
Abstract
Many mobility applications in smart cities are addressed as optimization problems. However, often, these problems are fragile due to their large-scale and non-convex nature, and also due to uncertainties arising because of human activity. In this paper, we apply a model-based Markov-decision-process (MDP) closed-loop identification algorithm to augment classical optimizers, with a view to alleviating this fragility. Specifically, we use deterministic optimal solutions provided by classical optimizers as initial guesses for MDP's policies, which are then amended as a result of online interaction with the environment to cope with uncertainty. Applications are described from niche of smart mobility problems, and numerical results are provided.