Function: Learning Rate — func

$$Q_{new} = Q_{old} + \alpha \cdot (R - Q_{old})$$

Usage

func_alpha(qvalue, reward, params, ...)

Arguments

qvalue: The estimated expected value of taking action(a) at trial(t).
reward: The feedback received by the agent from the environment at trial(t) following the execution of action(a)
params: Parameters used by the model’s internal functions, see params
...: Subject ID, Block ID, Trial ID, and any additional information defined by the user.

Body

func_alpha <- function(
    qvalue,
    reward,
    params,
    ...
){
  # if you need extra information
  # e.g.
  # Trial <- idinfo["Trial"]
  # Frame <- exinfo["Frame"]

  alpha     <-  multiRL:::get_param(params, "alpha")
  alphaN    <-  multiRL:::get_param(params, "alphaN")
  alphaP    <-  multiRL:::get_param(params, "alphaP")

  # Determine the model currently in use based on which parameters are free.
  if (
    !(is.na(alpha)) && is.na(alphaN) && is.na(alphaP)
  ) {
    model <- "TD"
  } else if (
    is.na(alpha) && !(is.na(alphaN)) && !(is.na(alphaP))
  ) {
    model <- "RSTD"
  } else {
    stop("Unknown Model! Plase modify your learning rate function")
  }

  # TD
  if (model == "TD") {
    update <- qvalue + alpha   * (reward - qvalue)
    # RSTD
  } else if (model == "RSTD" && reward < qvalue) {
    update <- qvalue + alphaN * (reward - qvalue)
  } else if (model == "RSTD" && reward >= qvalue) {
    update <- qvalue + alphaP * (reward - qvalue)
  }

  return(update)
}