Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.16301 |
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Inhaltsangabe:
- 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.