# Deep Reinforcement Learning in a Monetary Model

arXiv: https://arxiv.org/abs/2104.09368 · HTML: https://mlsanity.com/paper/2104.09368/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)

## Built on by (most cited first, 11 of 11)

- [Reinforcement Learning for Economic Policy: A New Frontier?](https://mlsanity.com/paper/2206.08781/) (2022)
- [Can an AI agent hit a moving target?](https://mlsanity.com/paper/2110.02474/) (2021)
- [EconGym: A Scalable AI Testbed with Diverse Economic Tasks](https://mlsanity.com/paper/2506.12110/) (2025)
- [Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making](https://mlsanity.com/paper/2511.12876/) (2025)
- [Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics](https://mlsanity.com/paper/2512.18892/) (2025)
- [Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.01163/) (2022)
- [Machine Learning for Economics Research: When What and How?](https://mlsanity.com/paper/2304.00086/) (2023)
- [Empirical Equilibria in Agent-based Economic systems with Learning agents](https://mlsanity.com/paper/2408.12038/) (2024)
- [Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.12272/) (2025)
- [Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games](https://mlsanity.com/paper/2403.12093/) (2024)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)

## Abstract (arXiv)

We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve their dynamic optimisation problem by interacting with the model environment, of which they have no a priori knowledge. Deep reinforcement learning offers a flexible yet principled way to model bounded rationality within this general class of models. We apply our proposed approach to a classical model from the adaptive learning literature in macroeconomics which looks at the interaction of monetary and fiscal policy. We find that, contrary to adaptive learning, the artificially intelligent household can solve the model in all policy regimes.