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CITATION.cff
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cff-version: 1.2.0
title: >-
Case study for
MARL-iDR-Multi-Agent-Reinforcement-Learning-for-Incentive-based-Residential-Demand-Response
message: 'If you use this software, please cite it as below.'
type: software
authors:
- given-names: Jasper
family-names: 'van Tilburg '
affiliation: University of Technology Delft
- given-names: Luciano Cavalcante
family-names: Siebert
email: L.CavalcanteSiebert@tudelft.nl
affiliation: University of Technology Delft
orcid: 'https://orcid.org/0000-0002-7531-3154'
- given-names: Jochen L.
family-names: Cremer
email: j.l.cremer@tudelft.nl
affiliation: University of Technology Delft
orcid: 'https://orcid.org/0000-0001-9284-5083'
repository-code: >-
https://github.com/TU-Delft-AI-Energy-Lab/MARL-iDR-Multi-Agent-Reinforcement-Learning-for-Incentive-based-Residential-Demand-Response
abstract: >-
This repository contains the code for the most recent
versions of the model for the paper:
Jasper van Tilburg, Luciano C. Siebert, Jochen L. Cremer,
"MARL-iDR: Multi-Agent Reinforcement Learning for
Incentive-based Residential Demand Response" to appear at
IEEE PowerTech 2023, Belgrade, Serbia
keywords:
- Reinforcement Learning
- Energy Community
- Demand Response
license: MIT