# Reinforcement Learning in Economics and Finance

arXiv: https://arxiv.org/abs/2003.10014 · HTML: https://mlsanity.com/paper/2003.10014/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 30 of 58)

- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Proximal Point Imitation Learning](https://mlsanity.com/paper/2209.10968/) (2022)
- [Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning](https://mlsanity.com/paper/2209.10974/) (2022)
- [Reinforcement Learning for Mean Field Games, with Applications to Economics](https://mlsanity.com/paper/2106.13755/) (2021)
- [Imitation Learning in Discounted Linear MDPs without exploration assumptions](https://mlsanity.com/paper/2405.02181/) (2024)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective](https://mlsanity.com/paper/2402.00262/) (2024)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)
- [A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management](https://mlsanity.com/paper/2306.07542/) (2023)
- [Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots](https://mlsanity.com/paper/2409.10277/) (2024)
- [Learning Eco-Driving Strategies at Signalized Intersections](https://mlsanity.com/paper/2204.12561/) (2022)
- [Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples](https://mlsanity.com/paper/2310.07747/) (2023)
- [Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2405.16390/) (2024)
- [Risk-Aware Reinforcement Learning through Optimal Transport Theory](https://mlsanity.com/paper/2309.06239/) (2023)
- [Efficient Risk-sensitive Planning via Entropic Risk Measures](https://mlsanity.com/paper/2502.20423/) (2025)
- [Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis](https://mlsanity.com/paper/2403.08955/) (2024)
- [Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation](https://mlsanity.com/paper/2211.15414/) (2022)
- [(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions](https://mlsanity.com/paper/2311.17165/) (2023)
- [Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling](https://mlsanity.com/paper/2405.02161/) (2024)
- [Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2503.08934/) (2025)
- [Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines](https://mlsanity.com/paper/2109.10900/) (2021)
- [MOORL: A Framework for Integrating Offline-Online Reinforcement Learning](https://mlsanity.com/paper/2506.09574/) (2025)
- [How to Choose a Reinforcement-Learning Algorithm](https://mlsanity.com/paper/2407.20917/) (2024)
- [Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis](https://mlsanity.com/paper/2505.12462/) (2025)
- [Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach](https://mlsanity.com/paper/2510.18687/) (2025)
- [An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design](https://mlsanity.com/paper/2511.19726/) (2025)
- [Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts](https://mlsanity.com/paper/2109.01178/) (2021)
- [A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making](https://mlsanity.com/paper/2304.10590/) (2023)
- [Systematic Review on Reinforcement Learning in the Field of Fintech](https://mlsanity.com/paper/2305.07466/) (2023)

## Abstract (arXiv)

Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices. In reinforcement learning, his actions have consequences: they influence not only rewards, but also future states of the world. The goal of reinforcement learning is to find an optimal policy -- a mapping from the states of the world to the set of actions, in order to maximize cumulative reward, which is a long term strategy. Exploring might be sub-optimal on a short-term horizon but could lead to optimal long-term ones. Many problems of optimal control, popular in economics for more than forty years, can be expressed in the reinforcement learning framework, and recent advances in computational science, provided in particular by deep learning algorithms, can be used by economists in order to solve complex behavioral problems. In this article, we propose a state-of-the-art of reinforcement learning techniques, and present applications in economics, game theory, operation research and finance.