Optimizing Market Making using Multi-Agent Reinforcement Learning
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Abstract (arXiv)
In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold an asset. The micro-agent optimizes on placing limit orders within the limit order book. For the context of this paper, the proposed framework is applied and studied on the Bitcoin cryptocurrency market. The goal of this paper is to show that reinforcement learning is a viable strategy that can be applied to complex problems (with complex environments) such as market making.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Dec 2018.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 2; 2025: 2.
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- 2021Recent Advances in Reinforcement Learning in Financecited by 110
- 2024A Review of Reinforcement Learning in Financial Applicationscited by 13
- 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Surveycited by 11
- 2021Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPscited by 8
- 2023IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Makingcited by 6
- 2022Market Making with Scaled Beta Policiescited by 5
- 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
- 2022Model-based gym environments for limit order book tradingcited by 3
- 2025Multi-Agent Reinforcement Learning for Market Making: Competition without Collusioncited by 2
- 2021Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Marketcited by 1
- 2023Integrating Tick-level Data and Periodical Signal for High-frequency Market Makingcited by 1
- 2023INTAGS: Interactive Agent-Guided Simulationcited by 1
- 2025Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion29 Oct 2025
- 2025Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics15 Sep 2025
- 2024A Review of Reinforcement Learning in Financial Applications1 Nov 2024
- 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey20 Aug 2024
- 2023INTAGS: Interactive Agent-Guided Simulation4 Sep 2023
- 2023IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making17 Aug 2023
- 2023Integrating Tick-level Data and Periodical Signal for High-frequency Market Making19 Jun 2023
- 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges17 May 2023
- 2022Model-based gym environments for limit order book trading16 Sep 2022
- 2022Market Making with Scaled Beta Policies7 Jul 2022
- 2021Recent Advances in Reinforcement Learning in Finance8 Dec 2021
- 2021Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market8 Dec 2021
1 more
- 2021Recent Advances in Reinforcement Learning in Financecited by 110
- 2021Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPscited by 8
- 2023IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Makingcited by 6
- 2022Market Making with Scaled Beta Policiescited by 5
- 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
- 2022Model-based gym environments for limit order book tradingcited by 3
- 2025Multi-Agent Reinforcement Learning for Market Making: Competition without Collusioncited by 2
- 2021Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Marketcited by 1
- 2023Integrating Tick-level Data and Periodical Signal for High-frequency Market Makingcited by 1
- 2023INTAGS: Interactive Agent-Guided Simulationcited by 1
- 2025Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion29 Oct 2025
- 2023INTAGS: Interactive Agent-Guided Simulation4 Sep 2023
- 2023IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making17 Aug 2023
- 2023Integrating Tick-level Data and Periodical Signal for High-frequency Market Making19 Jun 2023
- 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges17 May 2023
- 2022Model-based gym environments for limit order book trading16 Sep 2022
- 2022Market Making with Scaled Beta Policies7 Jul 2022
- 2021Recent Advances in Reinforcement Learning in Finance8 Dec 2021
- 2021Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market8 Dec 2021
- 2021Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs20 Feb 2021