\(\epsilon-first\):
$$ P(x) = \begin{cases} i \le \text{threshold}, & x=1 \\ i > \text{threshold}, & x=0 \end{cases} $$
\(\epsilon-greedy\):
$$ P(x) = \begin{cases} \epsilon, & x=1 \\ 1-\epsilon, & x=0 \end{cases} $$
\(\epsilon-decreasing\):
$$ P(x) = \begin{cases} \frac{1}{1+\epsilon \cdot i}, & x=1 \\ \frac{\epsilon \cdot i}{1+\epsilon \cdot i}, & x=0 \end{cases} $$
func_epsilon(shown, rownum, params, ...)An int, either 0 or 1, indicating exploration or
exploitation on the current trial.
Which options shown in this trial.
The trial number
Parameters used by the model's internal functions, see params
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.
func_epsilon <- function(
shown,
rownum,
params,
...
){ 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(try)
}