Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support-Reference-Cited by-同舟云学术

Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support

Published:2024-07-09 Issue: Volume: Page:1-19
ISSN:1747-7778
Container-title:Journal of Simulation
language:en
Short-container-title:Journal of Simulation

Author:

Olmez Sedar¹²,Heppenstall Alison³,Ge Jiaqi¹,Elsenbroich Corinna⁴,Birks Dan²⁵

Affiliation:

1. School of Geography, University of Leeds, Leeds, UK

2. The Alan Turing Institute, British Library, London, UK

3. School of Social Political Sciences, University of Glasgow, Glasgow, Scotland

4. School of Health and Wellbeing, University of Glasgow, Glasgow, Scotland

5. School of Law, University of Leeds, Leeds, UK

Funder

Economic and Social Research Council

Medical Research Council

Chief Scientist Office

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17477778.2024.2375446

Reference90 articles.

1. Agarwal, A., Kakade, S. M., Lee, J., & Mahajan, G. (2020). Optimality and approximation with policy gradient methods in markov decision processes. Conference on learning theory, Graz, Austria.

2. Almahamid, F., & Grolinger, K. (2021, 9). Reinforcement learning algorithms: An overview and classification. Canadian Conference on Electrical and Computer Engineering , 2021- September, Toronto, Canada.

3. Axtell, R. L. (2014). An agent-based model of the housing market bubble in metropolitan. SSRN.

4. Bae, J. W., Paik, E., Dongoh, K., Jung, J., & Lee, C. H. (2019, 1). Simulation framework for self-evolving agent-based models: A case study of housing market model. Proceedings - Winter Simulation Conference, Gothenburg, Sweden, 2018–December, (pp. 1120–1131).