#' @export
ContextualLinearBandit <- R6::R6Class(
"ContextualLinearBandit",
inherit = Bandit,
class = FALSE,
public = list(
rewards = NULL,
betas = NULL,
sigma = NULL,
binary = NULL,
weights = NULL,
class_name = "ContextualLinearBandit",
initialize = function(k, d, sigma = 0.1, binary_rewards = FALSE) {
self$k <- k
self$d <- d
self$sigma <- sigma
self$binary <- binary_rewards
},
post_initialization = function() {
self$betas <- matrix(runif(self$d*self$k, -1, 1), self$d, self$k)
self$betas <- self$betas / norm(self$betas, type = "2")
},
get_context = function(t) {
X <- rnorm(self$d)
self$weights <- X %*% self$betas
reward_vector <- self$weights + rnorm(self$k, sd = self$sigma)
if (isTRUE(self$binary)) {
self$rewards <- rep(0,self$k)
self$rewards[which_max_tied(reward_vector)] <- 1
} else {
self$rewards <- reward_vector
}
context <- list(
k = self$k,
d = self$d,
X = X
)
},
get_reward = function(t, context_common, action) {
rewards <- self$rewards
optimal_arm <- which_max_tied(self$weights)
reward <- list(
reward = rewards[action$choice],
optimal_arm = optimal_arm,
optimal_reward = rewards[optimal_arm]
)
}
)
)
#' Bandit: ContextualLinearBandit
#'
#' Samples data from linearly parameterized arms.
#'
#' The reward for context X and arm j is given by X^T beta_j, for some latent
#' set of parameters {beta_j : j = 1, ..., k}. The beta's are sampled uniformly
#' at random, the contexts are Gaussian, and sigma-noise is added to the rewards.
#'
#' @name ContextualLinearBandit
#'
#' @section Usage:
#' \preformatted{
#' bandit <- ContextualLinearBandit$new(k, d, sigma = 0.1, binary_rewards = FALSE)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#'
#' \item{\code{k}}{
#' integer; number of bandit arms
#' }
#' \item{\code{d}}{
#' integer; number of contextual features
#' }
#' \item{\code{sigma}}{
#' numeric; standard deviation of the additive noise. Set to zero for no noise. Default is \code{0.1}
#' }
#' \item{\code{binary_rewards}}{
#' logical; when set to \code{FALSE} (default) ContextualLinearBandit generates Gaussian rewards.
#' When set to \code{TRUE}, rewards are binary (0/1).
#' }
#'
#' }
#'
#' @section Methods:
#'
#' \describe{
#'
#' \item{\code{new(k, d, sigma = 0.1, binary_rewards = FALSE)}}{ generates and
#' instantializes a new \code{ContextualLinearBandit} instance. }
#'
#' \item{\code{get_context(t)}}{
#' argument:
#' \itemize{
#' \item \code{t}: integer, time step \code{t}.
#' }
#' returns a named \code{list}
#' containing the current \code{d x k} dimensional matrix \code{context$X},
#' the number of arms \code{context$k} and the number of features \code{context$d}.
#' }
#'
#' \item{\code{get_reward(t, context, action)}}{
#' arguments:
#' \itemize{
#' \item \code{t}: integer, time step \code{t}.
#' \item \code{context}: list, containing the current \code{context$X} (d x k context matrix),
#' \code{context$k} (number of arms) and \code{context$d} (number of context features)
#' (as set by \code{bandit}).
#' \item \code{action}: list, containing \code{action$choice} (as set by \code{policy}).
#' }
#' returns a named \code{list} containing \code{reward$reward} and, where computable,
#' \code{reward$optimal} (used by "oracle" policies and to calculate regret).
#' }
#'
#' \item{\code{post_initialization()}}{
#' initializes \code{d x k} beta matrix.
#' }
#
#' }
#'
#' @references
#'
#' Riquelme, C., Tucker, G., & Snoek, J. (2018). Deep Bayesian Bandits Showdown: An Empirical Comparison of
#' Bayesian Deep Networks for Thompson Sampling. arXiv preprint arXiv:1802.09127.
#'
#' Implementation follows
#' \url{https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits}
#'
#' @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}}
#'
#' @examples
#' \dontrun{
#'
#' horizon <- 800L
#' simulations <- 30L
#'
#' bandit <- ContextualLinearBandit$new(k = 5, d = 5)
#'
#' agents <- list(Agent$new(EpsilonGreedyPolicy$new(0.1), bandit),
#' Agent$new(LinUCBDisjointOptimizedPolicy$new(0.6), bandit))
#'
#' simulation <- Simulator$new(agents, horizon, simulations)
#' history <- simulation$run()
#'
#' plot(history, type = "cumulative", regret = FALSE, rate = TRUE, legend_position = "right")
#' }
NULL
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