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#' @export
ContextualEpochGreedyPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
sZl = NULL,
exploration_phase = NULL,
class_name = "ContextualEpochGreedyPolicy",
initialize = function(sZl = 10) {
super$initialize()
self$sZl <- sZl
},
set_parameters = function(context_params) {
d <- context_params$d
self$theta_to_arms <- list('A' = diag(1,d,d), 'b' = rep(0,d))
},
get_action = function(t, context) {
if(t==1 || t%%self$sZl==0) self$exploration_phase = TRUE
if (!isTRUE(self$exploration_phase)) {
expected_rewards <- rep(0.0, context$k)
for (arm in 1:context$k) {
Xa <- get_arm_context(context, arm)
A <- self$theta$A[[arm]]
b <- self$theta$b[[arm]]
A_inv <- inv(A)
theta_hat <- A_inv %*% b
expected_rewards[arm] <- Xa %*% theta_hat
}
action$choice <- which_max_tied(expected_rewards)
} else {
self$action$choice <- sample.int(context$k, 1, replace = TRUE)
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
reward <- reward$reward
Xa <- get_arm_context(context, arm)
if (isTRUE(self$exploration_phase)) {
inc(self$theta$A[[arm]]) <- outer(Xa, Xa)
inc(self$theta$b[[arm]]) <- reward * Xa
self$exploration_phase <- FALSE
}
self$theta
}
)
)
#' Policy: A Time and Space Efficient Algorithm for Contextual Linear Bandits
#'
#' @name ContextualEpochGreedyPolicy
#'
#'
#' @section Usage:
#' \preformatted{
#' policy <- ContextualEpochGreedyPolicy$new(sZl = 10)
#' }
#'
#' @seealso
#'
#' Core contextual classes: \code{\link{Bandit}}, \code{\link{Policy}}, \code{\link{Simulator}},
#' \code{\link{Agent}}, \code{\link{History}}, \code{\link{Plot}}
#'
#' Bandit subclass examples: \code{\link{BasicBernoulliBandit}}, \code{\link{ContextualLogitBandit}},
#' \code{\link{OfflineReplayEvaluatorBandit}}
#'
#' Policy subclass examples: \code{\link{EpsilonGreedyPolicy}}, \code{\link{ContextualLinTSPolicy}}
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