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 MDP update loop is executed in C++ or in R.
...: Additional arguments passed to internal functions.

Example

 # multiRL.model
 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(
       Q0 = NA,
       lapse = 0.01,
       threshold = 1,
       bonus = 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"
   ),
   engine = "Cpp"
 )

 multiRL.summary <- multiRL::summary(multiRL.model)