Adaptive Dueling Double Deep Q-networks in Uniswap V3 Replication and Extension with Mamba
This work addresses liquidity provision optimization in decentralized finance (Uniswap V3) for traders and liquidity providers, but it is incremental as it builds directly on existing research.
The paper replicates and extends a prior study on adaptive liquidity provision in Uniswap V3 using deep reinforcement learning, proposing a new model that combines Mamba with DDQN and a new reward function, which shows stronger theoretical support and better performance in some tests.
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.