In this repository you find a comprehensive and hopefully growing list of relevant literature in the sphere of Economics, Finance and Reinforcement Learning.
- Literature Reviews
- Macroeconomics
- Game Theory
- Price Theory
- Financial Markets
- Economic Policy
- Other Relevant Material
- How To Participate
- Contributions
Mosavi, Amirhosein; Faghan, Yaser; Ghamisi, Pedram; Duan, Puhong; Ardabili, Sina Faizollahzadeh; Salwana, Ely; Band, Shahab S. (2020): Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics. In Mathematics 8 (10), p. 1640. DOI: 10.3390/math8101640.
Charpentier, Arthur; Élie, Romuald; Remlinger, Carl (2021): Reinforcement Learning in Economics and Finance. In Comput Econ, pp. 1–38. DOI: 10.1007/s10614-021-10119-4.
Tilbury, Callum Rhys (2022): Reinforcement Learning for Economic Policy: A New Frontier? Available online at http://arxiv.org/pdf/2206.08781.pdf.
Hambly, Ben; Xu, Renyuan; Yang, Huining (2023): Recent advances in reinforcement learning in finance. Mathematical Finance DOI: 10.1111/mafi.12382 ; Also preprint available online at https://arxiv.org/pdf/2112.04553.pdf
Chen, Mingli; Joseph, Andreas; Kumhof, Michael; Pan, Xinlei; Shi, Rui; Zhou, Xuan (2021): Deep Reinforcement Learning in a Monetary Model. Available online at https://arxiv.org/pdf/2104.09368.
Hill, Edward; Bardoscia, Marco; Turrell, Arthur (2021): Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning. Available online at http://arxiv.org/pdf/2103.16977v1.
Curry, Michael; Trott, Alexander; Phade, Soham; Bai, Yu; Zheng, Stephan (2022): Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning. Available online at https://arxiv.org/pdf/2201.01163.
Atashbar, Tohid; Shi, Rui Aruhan (2023): AI and Macroeconomic Modeling : Deep Reinforcement Learning in an RBC model. International Monetary Fund (WP/22/40). Available online at https://www.imf.org/en/Publications/WP/Issues/2023/02/24/AI-and-Macroeconomic-Modeling-Deep-Reinforcement-Learning-in-an-RBC-model-530084
Erev, Ido; Roth, Alvin E. (1998): Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria. In The American Economic Review 88 (4), pp. 848–881. Available online at http://www.jstor.org/stable/117009.
Franke, Reiner (2003): Reinforcement learning in the El Farol model. In Journal of Economic Behavior & Organization 51 (3), pp. 367–388. DOI: 10.1016/S0167-2681(02)00152-X.
Calvano, Emilio; Calzolari, Giacomo; Denicolò, Vincenzo; Pastorello, Sergio (2020): Artificial Intelligence, Algorithmic Pricing, and Collusion. In American Economic Review 110 (10), pp. 3267–3297. DOI: 10.1257/aer.20190623.
Danassis, Panayiotis; Filos-Ratsikas, Aris; Faltings, Boi (2021): Achieving Diverse Objectives with AI-driven Prices in Deep Reinforcement Learning Multi-agent Markets. Available online at https://arxiv.org/pdf/2106.06060.
Hettich, Matthias (2021): Algorithmic Collusion: Insights from Deep Learning. Available online at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3785966.
Werner, Tobias (2021): Algorithmic and Human Collusion. Available online at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3960738.
Johnson, Justin P.; Rhodes, Andrew; Wildenbeest, Matthijs (2023): Platform Design When Sellers Use Pricing Algorithms. In ECTA 91 (5), pp. 1841–1879. DOI: 10.3982/ECTA19978.
Frick, Kevin Michael (2023): Convergence Rates and Collusive Outcomes of Pricing Algorithms. Available online at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4527452
Bekiros, Stelios D. (2010): Heterogeneous trading strategies with adaptive fuzzy Actor–Critic reinforcement learning: A behavioral approach. In Journal of Economic Dynamics and Control 34 (6), pp. 1153–1170.
Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai (2017): Deep Direct Reinforcement Learning for Financial Signal Representation and Trading. In IEEE Trans. Neural Netw. Learning Syst. 28 (3), pp. 653–664. DOI: 10.1109/tnnls.2016.2522401.
Guéant, Olivier; Manziuk, Iuliia (2019): Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality. In Applied Mathematical Finance 26 (5), pp. 387–452. DOI: 10.1080/1350486X.2020.1714455.
Buehler, H.; Gonon, L.; Teichmann, J.; Wood, B. (2019): Deep hedging. In Quantitative Finance 19 (8), pp. 1271–1291. DOI: 10.1080/14697688.2019.1571683.
Azhikodan, Akhil Raj; Bhat, Anvitha G. K.; Jadhav, Mamatha V. (2019): Stock Trading Bot Using Deep Reinforcement Learning. In H. S. Saini, Rishi Sayal, A. Govardhan, Rajkumar Buyya (Eds.): Innovations in Computer Science and Engineering. Singapore, 2019. Singapore: Springer Singapore, pp. 41–49.
Zheng, Stephan; Trott, Alexander; Srinivasa, Sunil; Naik, Nikhil; Gruesbeck, Melvin; Parkes, David C.; Socher, Richard (2020): The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies. Available online at https://arxiv.org/pdf/2004.13332.
Zheng, Stephan; Trott, Alexander; Srinivasa, Sunil; Parkes, David C.; Socher, Richard (2021): The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning. Available online at https://arxiv.org/pdf/2108.02755.
Hinterlang, Natascha; Tänzer, Alina (2021): Optimal Monetary Policy Using Reinforcement Learning. Deutsche Bundesbank Discussion Paper, 51/2021. Available online at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4025682.
This material has helped me and others a lot in some of my applications and my understanding of reinforcement learning algorithms:
Henderson, Peter; Islam, Riashat; Bachman, Philip; Pineau, Joelle; Precup, Doina; Meger, David (2018): Deep Reinforcement Learning That Matters. In AAAI 32 (1). DOI: 10.1609/aaai.v32i1.11694.
Engstrom, Logan; Ilyas, Andrew; Santurkar, Shibani; Tsipras, Dimitris; Janoos, Firdaus; Rudolph, Larry; Madry, Aleksander (2019): Implementation Matters in Deep RL: A Case Study on PPO and TRPO. In International Conference on Learning Representations. Available online at https://openreview.net/forum?id=r1etN1rtPB.
Andrychowicz, Marcin; Raichuk, Anton; Stańczyk, Piotr; Orsini, Manu; Girgin, Sertan; Marinier, Raphael et al. (2020): What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. Available online at https://arxiv.org/pdf/2006.05990.
Pardo, Fabio; Tavakoli, Arash; Levdik, Vitaly; Kormushev, Petar (2018): Time Limits in Reinforcement Learning. In International Conference on Machine Learning, pp. 4045–4054. Available online at http://proceedings.mlr.press/v80/pardo18a
Petrazzini, Irving G. B.; Antonelo, Eric A. (2021): Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution. Available online at https://arxiv.org/pdf/2111.02202.
Abbas, Zaheer; Zhao, Rosie; Modayil, Joseph; White, Adam; Machado, Marlos C. (2023): Loss of Plasticity in Continual Deep Reinforcement Learning. Available online at http://arxiv.org/pdf/2303.07507v1.
Dohare, Shibhansh; Hernandez-Garcia, J. Fernando; Rahman, Parash; Sutton, Richard S.; Mahmood, A. Rupam (2023): Loss of Plasticity in Deep Continual Learning. Available online at http://arxiv.org/pdf/2306.13812v2.
This collection is an ongoing by-product of my PhD program and makes no claim to ever be complete. Far from it and this is why I need your help!
If you have spotted a really interesting application of reinforcement learning in economics or finance or if you stumbled upon a really helpful paper that proposes a new RL method, just propose it via a GitHub Issue and I will add it to this list. Also, if you find any mistakes (e.g. wrong link or some paper has been published, yet) just create an issue. You can also propose changes via a pull request. Further, I am happy to hear from you about further improvements to this GitHub repository!
The goal of it all is to have a kind of curated list of relevant literature so that novices can easily access the small field and seniors find new publications easily.
Sincere thanks to the following contributors:
| GitHub-Username | Suggested paper(s) |
|---|---|
| @nehalahmedshaikh | Atashbar, Shi (2023) |
| @Jocho-Smith | Hambly, Ben (2023) |