# Right Place, Right Time: Market Simulation-based RL for Execution Optimisation

arXiv: https://arxiv.org/abs/2510.22206 · HTML: https://mlsanity.com/paper/2510.22206/

## Key facts

- Cited by 2 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 5 of its 20 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## 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, 5 of 5)

- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges](https://mlsanity.com/paper/2411.18892/) (2024)
- [Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning](https://mlsanity.com/paper/2007.01623/) (2020)
- [Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks](https://mlsanity.com/paper/2311.11913/) (2023)
- [Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior](https://mlsanity.com/paper/2403.19781/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agentic Trading: When LLM Agents Meet Financial Markets](https://mlsanity.com/paper/2605.19337/) (2026)
- [FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching](https://mlsanity.com/paper/2608.13096/) (2026)

## Abstract (arXiv)

Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms grow more sophisticated, optimising them becomes increasingly challenging. In this work, we present a reinforcement learning (RL) framework for discovering optimal execution strategies, evaluated within a reactive agent-based market simulator. This simulator creates reactive order flow and allows us to decompose slippage into its constituent components: market impact and execution risk. We assess the RL agent's performance using the efficient frontier based on work by Almgren and Chriss, measuring its ability to balance risk and cost. Results show that the RL-derived strategies consistently outperform baselines and operate near the efficient frontier, demonstrating a strong ability to optimise for risk and impact. These findings highlight the potential of reinforcement learning as a powerful tool in the trader's toolkit.