SLiM-Gym: Reinforcement Learning for Population Genetics

Niko Zuppas; Bryan C. Carstens

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arXiv · 2504.16301

SLiM-Gym: Reinforcement Learning for Population Genetics

Niko Zuppas·Bryan C. Carstens

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Abstract

We introduce SLiM-Gym, a Python package for integrating reinforcement learning (RL) with forward-time population genetic simulations. Wright-Fisher evolutionary dynamics offer a tractable framework for modeling populations across discrete generations, yet applying RL to these systems requires a compatible training environment. SLiM-Gym connects the standardized RL interface provided by Gymnasium with the high-fidelity evolutionary simulations of SLiM, allowing agents to interact with evolving populations in real time. This framework enables the development and evaluation of RL-based strategies for understanding evolutionary processes.

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Niko Zuppas, Bryan C. Carstens. 2025-04-22. SLiM-Gym: Reinforcement Learning for Population Genetics. https://arxiv.org/abs/2504.16301

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