estimate_0_ENV: Tool for Generating an Environment for Models

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

Tool for Generating an Environment for Models

Description

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.

Usage

estimate_0_ENV(
  data,
  colnames = list(),
  behrule,
  funcs = list(),
  priors = list(),
  settings = list(),
  ...
)

Arguments

data	A data frame in which each row represents a single trial, see data
colnames	Column names in the data frame, see colnames
behrule	The agent's implicitly formed internal rule, see behrule
funcs	The functions forming the reinforcement learning model, see funcs
priors	Prior probability density function of the free parameters, see priors
settings	Other model settings, see settings
...	Additional arguments passed to internal functions.

Value

An environment, multiRL.env contains all variables required by the objective function and is used to isolate environments during parallel computation.

multiRL documentation built on March 31, 2026, 5:06 p.m.