用Mamba改进Uniswap V3的自动做市策略，提升收益稳定性。
Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mamba
- 融合Mamba与双深度Q网络，捕捉交易序列长期依赖。
- 新奖励函数使模型在部分测试中收益提升12.7%。
- 适合对去中心化交易所做市策略感兴趣的开发者。
本文复现并改进了《基于深度强化学习的Uniswap V3自适应流动性提供》一文。复现部分涵盖从Uniswap Subgraph获取数据、模型实现细节及结果分析。在此基础上，提出一种新结构：将Mamba与双深度Q网络（DDQN）结合，并设计新奖励函数。重新清洗数据后引入两个新基线进行对比。尽管尚未在所有数据集上测试，但新模型展现出更强理论支撑，在部分实验中表现优于原模型。
原文摘要 · Abstract (English)
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.
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