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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2504.16301 |
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| _version_ | 1866909588926758912 |
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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 |