mlsanityData to 14 Oct 2026

Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior

arXiv 2403.19781 ↗First posted 28 Mar 2024q-fin.TR cs.LG cs.MAcited by 3

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

Abstract (arXiv)

Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets. However, traditional rule-based market simulators often fall short in accurately capturing the dynamic behavior of market participants, particularly in response to external market impact events or changes in the behavior of other participants. In this study, we explore an agent-based simulation framework employing reinforcement learning (RL) agents. We present the implementation details of these RL agents and demonstrate that the simulated market exhibits realistic stylized facts observed in real-world markets. Furthermore, we investigate the behavior of RL agents when confronted with external market impacts, such as a flash crash. Our findings shed light on the effectiveness and adaptability of RL-based agents within the simulation, offering insights into their response to significant market events.

Key facts. Cited by 3 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 4 of its 30 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

4 of 30 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Learning to simulate realistic limit order book markets from data as a World Agentcited by 27
  3. 2021Towards a fully RL-based Market Simulatorcited by 10
  4. 2022Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulationscited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Makingcited by 113
  2. 2025Right Place, Right Time: Market Simulation-based RL for Execution Optimisationcited by 2
  3. 2026RAINO: Anchoring Agents in Reality, A Systematic Review and Conceptual Framework for Realism in Agent-Based Modellingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
  2. 2024Reinforcement Learning Framework for Quantitative Trading
  3. 2025From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Models
  4. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
  5. 2026Can Reinforcement Learning Efficiently Discover Price Manipulation?
  6. 2026Realistic Market Impact Modeling for Reinforcement Learning Trading Environments
  7. 2025When AI Trading Agents Compete: Adverse Selection of Meta-Orders by Reinforcement Learning-Based Market Making
  8. 2024A Review of Reinforcement Learning in Financial Applications