# A Review of Reinforcement Learning in Financial Applications

arXiv: https://arxiv.org/abs/2411.12746 · HTML: https://mlsanity.com/paper/2411.12746/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 27 of its 113 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 27 of 27)

- [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)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games](https://mlsanity.com/paper/1603.01121/) (2016)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance](https://mlsanity.com/paper/2011.09607/) (2020)
- [RLlib: Abstractions for Distributed Reinforcement Learning](https://mlsanity.com/paper/1712.09381/) (2017)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making](https://mlsanity.com/paper/2002.01751/) (2020)
- [Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization](https://mlsanity.com/paper/1901.08740/) (2019)
- [Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review](https://mlsanity.com/paper/2106.00123/) (2021)
- [MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System](https://mlsanity.com/paper/2007.05402/) (2020)
- [Model-based Reinforcement Learning for Predictions and Control for Limit Order Books](https://mlsanity.com/paper/1910.03743/) (2019)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)
- [No-Regret Reinforcement Learning with Heavy-Tailed Rewards](https://mlsanity.com/paper/2102.12769/) (2021)
- [Optimizing Market Making using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1812.10252/) (2018)
- [Testing Stationarity and Change Point Detection in Reinforcement Learning](https://mlsanity.com/paper/2203.01707/) (2022)
- [Learning Compact Models for Planning with Exogenous Processes](https://mlsanity.com/paper/1909.13870/) (2019)
- [TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow](https://mlsanity.com/paper/1709.02878/) (2017)
- [FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance](https://mlsanity.com/paper/2112.06753/) (2021)
- [Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling](https://mlsanity.com/paper/1911.05309/) (2019)
- [Optimal Market Making by Reinforcement Learning](https://mlsanity.com/paper/2104.04036/) (2021)
- [Towards Inverse Reinforcement Learning for Limit Order Book Dynamics](https://mlsanity.com/paper/1906.04813/) (2019)

## Built on by (most cited first, 13 of 13)

- [TrustTrade: Human-Inspired Selective Consensus Reduces Decision Uncertainty in LLM Trading Agents](https://mlsanity.com/paper/2603.22567/) (2026)
- [Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments](https://mlsanity.com/paper/2507.18606/) (2025)
- [Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach](https://mlsanity.com/paper/2510.26805/) (2025)
- [Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes](https://mlsanity.com/paper/2602.01295/) (2026)
- [AI Agents and Hard Choices](https://mlsanity.com/paper/2504.15304/) (2025)
- [Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization](https://mlsanity.com/paper/2509.00095/) (2025)
- [Increase Alpha: Performance and Risk of an AI-Driven Trading Framework](https://mlsanity.com/paper/2509.16707/) (2025)
- [FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management](https://mlsanity.com/paper/2510.02986/) (2025)
- [Towards Reinforcement Learning Based Log Loading Automation](https://mlsanity.com/paper/2510.26363/) (2025)
- [Reinforcement Learning-Guided Dynamic Multi-Graph Fusion for Evacuation Traffic Prediction](https://mlsanity.com/paper/2601.06664/) (2026)
- [RL-ABC: Reinforcement Learning for Accelerator Beamline Control](https://mlsanity.com/paper/2604.19146/) (2026)
- [Counterfactual Transport Flows for Offline Conservative Trajectory Refinement](https://mlsanity.com/paper/2606.09115/) (2026)
- [Reward Inflation: A Healthy Stimulus for Reinforcement Learning](https://mlsanity.com/paper/2610.02545/) (2026)

## Abstract (arXiv)

In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications. This approach has shown great potential to solve decision-making tasks in finance. In this survey, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared to traditional methods. Moreover, we identify challenges including explainability, Markov Decision Process (MDP) modeling, and robustness that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance.