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StoaEnv

 StoaEnv (n_founders=100, n_pop_size=200, n_chr=5, n_loci=1000,
          n_qtl_per_chr=50, total_gen=20, max_crossovers=10)

*A Gymnax environment for the ChewC breeding simulation.

The agent’s goal is to maximize the genetic gain of the population over a fixed number of generations by choosing the selection intensity at each step.*

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evaluate_constant_actions

 evaluate_constant_actions (env:__main__.StoaEnv, action_values,
                            num_episodes:int=32, seed:int=0,
                            confidence_level:float=0.95)

Evaluates constant actions over multiple episodes.

	Type	Default	Details
env	StoaEnv
action_values
num_episodes	int	32
seed	int	0
confidence_level	float	0.95	For confidence interval
Returns	DataFrame

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run_constant_action_episode

 run_constant_action_episode (env:__main__.StoaEnv, action_value:float,
                              rng_key:jax.Array)

Runs a single episode with a constant action and returns metrics and history.

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StoaEnv

 StoaEnv (n_founders=100, n_pop_size=200, n_chr=5, n_loci=1000,
          n_qtl_per_chr=50, total_gen=20, max_crossovers=10)

*A Gymnax environment for the ChewC breeding simulation.

The agent’s goal is to maximize the genetic gain of the population over a fixed number of generations by choosing the selection intensity at each step.*