# Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

arXiv: https://arxiv.org/abs/1906.11046 · HTML: https://mlsanity.com/paper/1906.11046/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Deep Multiagent Reinforcement Learning: Challenges and Directions](https://mlsanity.com/paper/2106.15691/) (2021)
- [One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2212.00124/) (2022)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy](https://mlsanity.com/paper/2511.12120/) (2025)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance](https://mlsanity.com/paper/2112.06753/) (2021)
- [Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance](https://mlsanity.com/paper/2307.03119/) (2023)
- [Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction](https://mlsanity.com/paper/2502.21186/) (2025)
- [A Modular Framework for Reinforcement Learning Optimal Execution](https://mlsanity.com/paper/2208.06244/) (2022)
- [Optimal Execution Using Reinforcement Learning](https://mlsanity.com/paper/2306.17178/) (2023)
- [Deviations from the Nash equilibrium in a two-player optimal execution game with reinforcement learning](https://mlsanity.com/paper/2408.11773/) (2024)
- [Offline Policy Optimization with Posterior Sampling](https://mlsanity.com/paper/2605.07393/) (2026)
- [Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief](https://mlsanity.com/paper/2606.00680/) (2026)

## Abstract (arXiv)

Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact and a trader's risk aversion. The main challenge in optimizing liquidation is to find an appropriate modeling system that can incorporate the complexities of the stock market and generate practical trading strategies. In this paper, we propose to use multi-agent deep reinforcement learning model, which better captures high-level complexities comparing to various machine learning methods, such that agents can learn how to make the best selling decisions. First, we theoretically analyze the Almgren and Chriss model and extend its fundamental mechanism so it can be used as the multi-agent trading environment. Our work builds the foundation for future multi-agent environment trading analysis. Secondly, we analyze the cooperative and competitive behaviours between agents by adjusting the reward functions for each agent, which overcomes the limitation of single-agent reinforcement learning algorithms. Finally, we simulate trading and develop an optimal trading strategy with practical constraints by using a reinforcement learning method, which shows the capabilities of reinforcement learning methods in solving realistic liquidation problems.