run_m: Step 1: Building reinforcement learning model

In multiRL: Reinforcement Learning Tools for Multi-Armed Bandit

Step 1: Building reinforcement learning model

Description

Step 1: Building reinforcement learning model

Usage

run_m(
  data,
  colnames = list(),
  behrule = list(),
  funcs = list(),
  params = list(),
  priors = list(),
  settings = list(),
  engine = "Cpp",
  ...
)

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
params	Parameters used by the model's internal functions, see params
priors	Prior probability density function of the free parameters, see priors
settings	Other model settings, see settings
engine	Specifies whether the core Markov Decision Process (MDP) update loop is executed in C++ or in R.
...	Additional arguments passed to internal functions.

Value

An S4 object of class multiRL.model.

input

An S4 object of class multiRL.input, containing the raw data, column specifications, parameters and ...

behrule

An S4 object of class multiRL.behrule, defining the latent learning rules.

result

An S4 object of class multiRL.result, storing trial-level outputs of the Markov Decision Process.

sumstat

An S4 object of class multiRL.sumstat, providing summary statistics across different estimation methods.

extra

A List containing additional user-defined information.

Examples

multiRL.model <- multiRL::run_m(
  data = multiRL::TAB[multiRL::TAB[, "Subject"] == 1, ],
  behrule = list(
    cue = c("A", "B", "C", "D"),
    rsp = c("A", "B", "C", "D")
  ),
  colnames = list(
    subid = "Subject", block = "Block", trial = "Trial",
    object = c("L_choice", "R_choice"), 
    reward = c("L_reward", "R_reward"),
    action = "Sub_Choose",
    exinfo = c("Frame", "NetWorth", "RT")
  ),
  params = list(
    free = list(
      alpha = 0.5,
      beta = 0.5
    ),
    fixed = list(
      gamma = 1, 
      delta = 0.1, 
      epsilon = NA_real_,
      zeta = 0
    ),
    constant = list(
      seed = 123,
      L = 0,
      penalty = 1,
      Q0 = NA_real_, 
      reset = NA_real_,
      lapse = 0.01,
      threshold = 1,
      bonus = 0,
      weight = 1,
      capacity = 0,
      sticky = 0
    )
  ),
  priors = list(
    alpha = function(x) {stats::dbeta(x, shape1 = 2, shape2 = 2, log = TRUE)}, 
    beta = function(x) {stats::dexp(x, rate = 1, log = TRUE)}
  ),
  settings = list(
    name = "TD",
    mode = "fitting",
    estimate = "MLE",
    policy = "off",
    system = c("RL", "WM")
 ),
 engine = "R"
)
 
multiRL.summary <- multiRL::summary(multiRL.model)

 

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