This function creates an independent R environment for each model (or object function) when searching for optimal parameters using an algorithm package. Such isolation is especially important when parameter optimization is performed in parallel across multiple subjects. The function transfers standardized input parameters into a dedicated environment, ensuring that each model is evaluated in a self-contained and interference-free context.
estimate_0_ENV(
data,
colnames = list(),
behrule,
funcs = list(),
priors = list(),
settings = list(),
...
)An environment, multiRL.env contains all variables
required by the objective function and is used to isolate environments
during parallel computation.
A data frame in which each row represents a single trial, see data
Column names in the data frame, see colnames
The agent’s implicitly formed internal rule, see behrule
The functions forming the reinforcement learning model, see funcs
Prior probability density function of the free parameters, see priors
Other model settings, see settings
Additional arguments passed to internal functions.