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.

Cet article explore les excursions dans le temps et leurs implications.

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