# Reinforcement Learning for Economic Policy: A New Frontier?

arXiv: https://arxiv.org/abs/2206.08781 · HTML: https://mlsanity.com/paper/2206.08781/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research](https://mlsanity.com/paper/2011.14826/) (2020)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Learning Reciprocity in Complex Sequential Social Dilemmas](https://mlsanity.com/paper/1903.08082/) (2019)
- [Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics](https://mlsanity.com/paper/2004.01509/) (2020)
- [Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning](https://mlsanity.com/paper/2112.10859/) (2021)
- [Deep Reinforcement Learning in a Monetary Model](https://mlsanity.com/paper/2104.09368/) (2021)
- [A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids](https://mlsanity.com/paper/2009.10905/) (2020)

## Built on by (most cited first, 4 of 4)

- [ADAGE: A generic two-layer framework for adaptive agent based modelling](https://mlsanity.com/paper/2501.09429/) (2025)
- [Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.00787/) (2024)
- [Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.12272/) (2025)
- [RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm](https://mlsanity.com/paper/2411.08392/) (2024)

## Abstract (arXiv)

Agent-based computational economics is a field with a rich academic history, yet one which has struggled to enter mainstream policy design toolboxes, plagued by the challenges associated with representing a complex and dynamic reality. The field of Reinforcement Learning (RL), too, has a rich history, and has recently been at the centre of several exponential developments. Modern RL implementations have been able to achieve unprecedented levels of sophistication, handling previously unthinkable degrees of complexity. This review surveys the historical barriers of classical agent-based techniques in economic modelling, and contemplates whether recent developments in RL can overcome any of them.