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Autori principali: Zuppas, Niko, Carstens, Bryan C.
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2504.16301
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author Zuppas, Niko
Carstens, Bryan C.
author_facet Zuppas, Niko
Carstens, Bryan C.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2504_16301
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SLiM-Gym: Reinforcement Learning for Population Genetics
Zuppas, Niko
Carstens, Bryan C.
Populations and Evolution
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.
title SLiM-Gym: Reinforcement Learning for Population Genetics
topic Populations and Evolution
url https://arxiv.org/abs/2504.16301