mlsanityData to 14 Oct 2026

A Review of Reinforcement Learning in Financial Applications

arXiv 2411.12746 ↗First posted 1 Nov 2024q-fin.CP cs.AI cs.LGcited by 13

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

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.

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.

Builds on

27 of 113 references.

References · 27

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  5. 2015Prioritized Experience Replaycited by 1,099
  6. 2016Deep Reinforcement Learning from Self-Play in Imperfect-Information Gamescited by 158
  7. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 150
  8. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  9. 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 87
  10. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 54
  11. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  12. 2019Reinforcement Learning for Market Making in a Multi-agent Dealer Marketcited by 39
15 more
  1. 2020Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Makingcited by 30
  2. 2019Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimizationcited by 22
  3. 2021Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Reviewcited by 17
  4. 2020MAPS: Multi-agent Reinforcement Learning-based Portfolio Management Systemcited by 15
  5. 2019Model-based Reinforcement Learning for Predictions and Control for Limit Order Bookscited by 14
  6. 2020Robust Market Making via Adversarial Reinforcement Learningcited by 14
  7. 2021No-Regret Reinforcement Learning with Heavy-Tailed Rewardscited by 14
  8. 2018Optimizing Market Making using Multi-Agent Reinforcement Learningcited by 13
  9. 2022Testing Stationarity and Change Point Detection in Reinforcement Learningcited by 12
  10. 2019Learning Compact Models for Planning with Exogenous Processescited by 11
  11. 2017TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlowcited by 9
  12. 2021FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Financecited by 7
  13. 2019Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Samplingcited by 4
  14. 2021Optimal Market Making by Reinforcement Learningcited by 4
  15. 2019Towards Inverse Reinforcement Learning for Limit Order Book Dynamicscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 6.

13 papers.

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  1. 2026TrustTrade: Human-Inspired Selective Consensus Reduces Decision Uncertainty in LLM Trading Agentscited by 3
  2. 2025Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environmentscited by 1
  3. 2025Reinforcement Learning for Accelerator Beamline Control: a simulation-based approachcited by 1
  4. 2026Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processescited by 1
  5. 2025AI Agents and Hard Choicescited by 0
  6. 2025Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimizationcited by 0
  7. 2025Increase Alpha: Performance and Risk of an AI-Driven Trading Frameworkcited by 0
  8. 2025FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Managementcited by 0
  9. 2025Towards Reinforcement Learning Based Log Loading Automationcited by 0
  10. 2026Reinforcement Learning-Guided Dynamic Multi-Graph Fusion for Evacuation Traffic Predictioncited by 0
  11. 2026RL-ABC: Reinforcement Learning for Accelerator Beamline Controlcited by 0
  12. 2026Counterfactual Transport Flows for Offline Conservative Trajectory Refinementcited by 0
1 more
  1. 2026Reward Inflation: A Healthy Stimulus for Reinforcement Learningcited by 0

Similar, not cited

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  1. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
  2. 2024Reinforcement Learning Framework for Quantitative Trading
  3. 2024Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity
  4. 2024Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
  5. 2024Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
  6. 2026Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
  7. 2025Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
  8. 2023PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets