Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mamba

ArXiv ID: 2511.22101 “View on arXiv”

Authors: Zhaofeng Zhang

Abstract

The report goes through the main steps of replicating and improving the article “Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning.” The replication part includes how to obtain data from the Uniswap Subgraph, details of the implementation, and comments on the results. After the replication, I propose a new structure based on the original model, which combines Mamba with DDQN and a new reward function. In this new structure, I clean the data again and introduce two new baselines for comparison. As a result, although the model has not yet been applied to all datasets, it shows stronger theoretical support than the original model and performs better in some tests.

Keywords: DeFi, Uniswap V3, Deep Reinforcement Learning, Liquidity Provision, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced reinforcement learning architectures (Dueling DDQN, Mamba) and complex mathematical formulations for Uniswap V3 modeling, indicating high math complexity. It is heavily data-driven with detailed backtest implementations, specific datasets from the Uniswap Subgraph, and comparative performance metrics, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal: Replicate & Improve<br>Adaptive Liquidity Provision in Uniswap V3"] --> B["Data Acquisition & Processing"]
    
    subgraph B [" "]
        direction LR
        B1["Uniswap V3 Subgraph"] --> B2["Data Cleaning & Normalization"]
    end

    B --> C{"Process Branch"}

    C --> D["Original Model Replication"]
    D --> D1["Implementation: DDQN"]
    D1 --> D2["Results Analysis"]

    C --> E["Enhanced Model Proposal"]
    E --> E1["New Architecture: Mamba + DDQN"]
    E1 --> E2["New Reward Function"]
    E2 --> E3["New Baselines for Comparison"]
    E3 --> E4["Theoretical Support & Performance Tests"]

    D2 --> F{"Key Findings & Outcomes"}
    E4 --> F

    F --> G["Stronger theoretical support than original"]
    F --> H["Better performance in test scenarios"]
    F --> I["Data limitations noted for full deployment"]
    
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style F fill:#ccf,stroke:#333,stroke-width:2px