Nothing
#' @export
LinUCBDisjointOptimizedPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
alpha = NULL,
class_name = "LinUCBDisjointOptimizedPolicy",
initialize = function(alpha = 1.0) {
super$initialize()
self$alpha <- alpha
},
set_parameters = function(context_params) {
ul <- length(context_params$unique)
self$theta_to_arms <- list( 'A_inv' = diag(1, ul, ul), 'b' = rep(0, ul))
},
get_action = function(t, context) {
expected_rewards <- rep(0.0, context$k)
for (arm in 1:context$k) {
Xa <- get_arm_context(context, arm, context$unique)
A_inv <- self$theta$A_inv[[arm]]
b <- self$theta$b[[arm]]
theta_hat <- A_inv %*% b
mu_hat <- Xa %*% theta_hat
sigma_hat <- sqrt(tcrossprod(Xa %*% A_inv, Xa))
expected_rewards[arm] <- mu_hat + self$alpha * sigma_hat
}
action$choice <- which_max_tied(expected_rewards)
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
reward <- reward$reward
Xa <- get_arm_context(context, arm, context$unique)
self$theta$A_inv[[arm]] <- sherman_morrisson(self$theta$A_inv[[arm]],Xa)
self$theta$b[[arm]] <- self$theta$b[[arm]] + reward * Xa
self$theta
}
)
)
#' Policy: LinUCB with unique linear models
#'
#' LinUCBDisjointOptimizedPolicy is an optimized R implementation of
#' "Algorithm 1 LinUCB" from Li (2010) "A contextual-bandit approach to
#' personalized news article recommendation.".
#'
#' Each time step t, \code{LinUCBDisjointPolicy} runs a linear regression per arm that produces coefficients
#' for each context feature \code{d}.
#' Next, \code{LinUCBDisjointPolicy} observes the new context, and generates a predicted payoff or reward
#' together with a confidence interval for each available arm. It then proceeds to choose the arm with the
#' highest upper confidence bound.
#'
#' @name LinUCBDisjointOptimizedPolicy
#'
#'
#' @section Usage:
#' \preformatted{
#' policy <- LinUCBDisjointOptimizedPolicy(alpha = 1.0)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#' \item{\code{alpha}}{
#' double, a positive real value R+;
#' Hyper-parameter adjusting the balance between exploration and exploitation.
#' }
#' \item{\code{name}}{
#' character string specifying this policy. \code{name}
#' is, among others, saved to the History log and displayed in summaries and plots.
#' }
#' }
#'
#' @section Parameters:
#'
#' \describe{
#' \item{\code{A}}{
#' d*d identity matrix
#' }
#' \item{\code{b}}{
#' a zero vector of length d
#' }
#' }
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{new(alpha = 1)}}{ Generates a new \code{LinUCBDisjointOptimizedPolicy} object. Arguments are
#' defined in the Argument section above.}
#' }
#'
#' \describe{
#' \item{\code{set_parameters()}}{each policy needs to assign the parameters it wants to keep track of
#' to list \code{self$theta_to_arms} that has to be defined in \code{set_parameters()}'s body.
#' The parameters defined here can later be accessed by arm index in the following way:
#' \code{theta[[index_of_arm]]$parameter_name}
#' }
#' }
#'
#' \describe{
#' \item{\code{get_action(context)}}{
#' here, a policy decides which arm to choose, based on the current values
#' of its parameters and, potentially, the current context.
#' }
#' }
#'
#' \describe{
#' \item{\code{set_reward(reward, context)}}{
#' in \code{set_reward(reward, context)}, a policy updates its parameter values
#' based on the reward received, and, potentially, the current context.
#' }
#' }
#'
#' @references
#'
#' Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010, April). A contextual-bandit approach to
#' personalized news article recommendation. In Proceedings of the 19th international conference on
#' World wide web (pp. 661-670). ACM.
#'
#' @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}}
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.