mlsanityData to 7 Oct 2026

Reinforcement Learning for Economic Policy: A New Frontier?

arXiv 2206.08781 ↗First posted 16 Jun 2022cs.LG cs.AI cs.MAcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 54 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

13 of 54 references.

References · 13

  1. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  2. 2017Deep Reinforcement Learning that Matterscited by 727
  3. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  4. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  5. 2019Challenges of Real-World Reinforcement Learningcited by 282
  6. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 206
  7. 2020Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Researchcited by 78
  8. 2020The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policiescited by 75
  9. 2019Learning Reciprocity in Complex Sequential Social Dilemmascited by 24
  10. 2020Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economicscited by 23
  11. 2021Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learningcited by 18
  12. 2021Deep Reinforcement Learning in a Monetary Modelcited by 10
1 more
  1. 2020A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Gridscited by 4

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025ADAGE: A generic two-layer framework for adaptive agent based modellingcited by 4
  2. 2024Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learningcited by 2
  3. 2025Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learningcited by 1
  4. 2024RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithmcited by 0