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

arXiv: https://arxiv.org/abs/2403.19781 · HTML: https://mlsanity.com/paper/2403.19781/

## 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.

## 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, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [Towards a fully RL-based Market Simulator](https://mlsanity.com/paper/2110.06829/) (2021)
- [Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations](https://mlsanity.com/paper/2210.07184/) (2022)

## Built on by (most cited first, 3 of 3)

- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [Right Place, Right Time: Market Simulation-based RL for Execution Optimisation](https://mlsanity.com/paper/2510.22206/) (2025)
- [RAINO: Anchoring Agents in Reality, A Systematic Review and Conceptual Framework for Realism in Agent-Based Modelling](https://mlsanity.com/paper/2606.05167/) (2026)

## 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.