---
title: Reinforcement Learning for Optimal Execution
url: https://www.ml-quant.com/papers/ssrn/4720833/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4720833
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4720833
featured: 2024-02-14
citations: unknown
topic: Trading, Microstructure & Execution
---


# Reinforcement Learning for Optimal Execution

A new actor-critic reinforcement learning algorithm is introduced for optimal execution problem, featuring a recalibration step for convergence and showing linear convergence under appropriate conditions.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4720833
- Identifier: SSRN 4720833
- Released: 2023-03-10
- First featured: Quant Letter No. 37 (2024-02-14): https://www.ml-quant.com/issues/2024-02-14/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

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