# Optimal Execution Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2306.17178 · HTML: https://mlsanity.com/paper/2306.17178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Double Deep Q-Learning for Optimal Execution](https://mlsanity.com/paper/1812.06600/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis](https://mlsanity.com/paper/1906.11046/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Meta Reinforcement Learning Approach to Goals-Based Wealth Management](https://mlsanity.com/paper/2605.02300/) (2026)

## Abstract (arXiv)

This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.