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

arXiv: https://arxiv.org/abs/2511.22101 · HTML: https://mlsanity.com/paper/2511.22101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 7 of 7)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Strategic Liquidity Provision in Uniswap v3](https://mlsanity.com/paper/2106.12033/) (2021)
- [Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning](https://mlsanity.com/paper/2309.10129/) (2023)
- [Quantformer: from attention to profit with a quantitative transformer trading strategy](https://mlsanity.com/paper/2404.00424/) (2024)
- [Uniswap Liquidity Provision: An Online Learning Approach](https://mlsanity.com/paper/2302.00610/) (2023)

## Abstract (arXiv)

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.