Function: Exploration or Exploitation — func

$\epsilon-first$:

$$ P(x) = \begin{cases} i \le \text{threshold}, & x=1 \\ i > \text{threshold}, & x=0 \end{cases} $$

$\epsilon-greedy$:

$$ P(x) = \begin{cases} \epsilon, & x=1 \\ 1-\epsilon, & x=0 \end{cases} $$

$\epsilon-decreasing$:

$$ P(x) = \begin{cases} \frac{1}{1+\epsilon \cdot i}, & x=1 \\ \frac{\epsilon \cdot i}{1+\epsilon \cdot i}, & x=0 \end{cases} $$

Usage

func_epsilon(shown, rownum, params, hidden, ...)

Arguments

shown

Which options shown in this trial.

rownum

The trial number

params

Parameters used by the model's internal functions, see params

hidden

All hidden variables within the MDP process belong here.

...

It currently contains the following information; additional information may be added in future package versions.

idinfo:
- subid
- block
- trial
exinfo: contains information whose column names are specified by the user.
- Frame
- RT
- NetWorth
- ...
behave: includes the following:
- action: the behavior performed by the human in the given trial.
- latent: the object updated by the agent in the given trial.
- simulation: the actual behavior performed by the agent.
- position: the position of the stimulus on the screen.
cue and rsp: Cues and responses within latent learning rules, see behrule
state: The state stores the stimuli shown in the current trial—split into components by underscores—and the rewards associated with them.

Value

A List

output [int]

Either 0 or 1, indicating exploration or exploitation on the current trial.
hidden [CharacterVector]

User-defined internal variables generated by this function. These represent intermediate (latent) states produced during computation, which can be read or modified by other functions in the MDP process.

Body

func_epsilon <- function(
    shown,
    rownum,
    params,
    hidden,
    ...
){

  list2env(list(...), envir = environment())

  # If you need extra information(...)
  # Column names may be lost(C++), indexes are recommended
  # e.g.
  # Trial  <- idinfo[3]
  # Frame  <- exinfo[1]
  # Action <- behave[1]

  epsilon   <-  params[["epsilon"]]
  threshold <-  params[["threshold"]]

  # Determine the model currently in use based on which parameters are free.
  if (is.na(epsilon) && threshold > 0) {
    model <- "first"
  } else if (!(is.na(epsilon)) && threshold == 0) {
    model <- "decreasing"
  } else if (!(is.na(epsilon)) && threshold == 1) {
    model <- "greedy"
  } else {
    stop("Unknown Model! Plase modify your learning rate function")
  }

  set.seed(rownum)
  # Epsilon-First:
  if (rownum <= threshold) {
    try <- 1
  } else if (rownum > threshold && model == "first") {
    try <- 0
    # Epsilon-Greedy:
  } else if (rownum > threshold && model == "greedy"){
    try <- as.integer(stats::runif(1) < epsilon)
    # Epsilon-Decreasing:
  } else if (rownum > threshold && model == "decreasing") {
    prob_explore <- 1 / (1 + epsilon * rownum)
    try <- as.integer(stats::runif(1) < prob_explore)
  }

  return(list(output = try, hidden = hidden))
}