mlsanityData to 14 Oct 2026

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

arXiv 2511.22101 ↗First posted 27 Nov 2025cs.LG q-fin.TRcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

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.

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.

Builds on

7 of 9 references.

References · 7

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2021Strategic Liquidity Provision in Uniswap v3cited by 11
  5. 2023Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learningcited by 6
  6. 2024Quantformer: from attention to profit with a quantitative transformer trading strategycited by 6
  7. 2023Uniswap Liquidity Provision: An Online Learning Approachcited by 5

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  6. 2024FDQN: A Flexible Deep Q-Network Framework for Game Automation
  7. 2024Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
  8. 2025Dueling Deep Reinforcement Learning for Financial Time Series