# SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity

arXiv: https://arxiv.org/abs/2609.17788 · HTML: https://mlsanity.com/paper/2609.17788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [Improved Price Oracles: Constant Function Market Makers](https://mlsanity.com/paper/2003.10001/) (2020)
- [Strategic Liquidity Provision in Uniswap v3](https://mlsanity.com/paper/2106.12033/) (2021)
- [Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design](https://mlsanity.com/paper/2204.00464/) (2022)
- [Optimal Routing for Constant Function Market Makers](https://mlsanity.com/paper/2204.05238/) (2022)
- [Competition between DEXs through Dynamic Fees](https://mlsanity.com/paper/2603.09669/) (2026)
- [Crypto x AI, AI x Crypto: A Survey](https://mlsanity.com/paper/2606.13892/) (2026)

## Abstract (arXiv)

We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of liquidity provision dynamically based on market conditions, which in turn, dictates how they earn fees. We decompose the microstructure of CPMs with CL in interactive components that allow researchers and practitioners to capture various economic settings. We employ a vectorized approach to optimize our environments, making them scalable for high dimensional Reinforcement Learning (RL) workflows that best describe sequential decision problems. We demonstrate the benefits of our environments by evaluating the performance of RL agents in CPMs with CL under uncertainty in market parameters.