Artificial Intelligence for Resource Allocation Tasks

MCML Authors

Niklas Strauß

Dr.

→ Group Matthias Schubert
 Spatial Artificial Intelligence

Abstract

This thesis presents deep reinforcement learning approaches for complex resource allocation tasks, including discrete, continuous, and resource collection problems. It introduces novel neural architectures achieving state-of-the-art results in spatial resource allocation, multi-agent collection, and dynamic ambulance redeployment, including electric ambulances. For continuous tasks like portfolio optimization, it proposes efficient methods to handle allocation constraints, ensuring compliance during training and deployment. (Shortened).

phdthesis Str24

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Dissertation

LMU München. Dec. 2024

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Authors

N. Strauß

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Links

DOI

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Research Area

 A3 | Computational Models