Nothing
#' @export
SoftmaxPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
tau = NULL,
class_name = "SoftmaxPolicy",
initialize = function(tau = 0.1) {
super$initialize()
self$tau <- tau
},
set_parameters = function(context_params) {
self$theta_to_arms <- list('n' = 0, 'mean' = 0)
},
get_action = function(t, context) {
exp_est_tau <- exp(unlist(self$theta$mean)/tau)
p <- exp_est_tau/sum(exp_est_tau)
action$choice <- categorical_draw(p)
return(action)
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
reward <- reward$reward
inc(self$theta$n[[arm]]) <- 1
inc(self$theta$mean[[arm]]) <- (reward - self$theta$mean[[arm]]) / self$theta$n[[arm]]
self$theta
},
categorical_draw = function(probs) {
arms <- length(probs)
cumulative_probability <- 0.0
z <- runif(1)
for (i in 1:arms) {
inc(cumulative_probability) <- probs[i]
if ( cumulative_probability > z ) return(i)
}
return(arms)
}
)
)
#' Policy: Softmax
#'
#' \code{SoftmaxPolicy} is very similar to \link{Exp3Policy}, but selects an arm based on the probability from
#' the Boltmann distribution. It makes use of a temperature parameter tau,
#' which specifies how many arms we can explore. When tau is high, all arms are explored equally,
#' when tau is low, arms offering higher rewards will be chosen.
#'
#' @name SoftmaxPolicy
#'
#' @section Usage:
#' \preformatted{
#' policy <- SoftmaxPolicy(tau = 0.1)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#' \item{\code{tau = 0.1}}{
#' double, temperature parameter tau specifies how many arms we can explore.
#' When tau is high, all arms are explored equally, when tau is low, arms offering higher
#' rewards will be chosen.
#' }
#' }
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{new(epsilon = 0.1)}}{ Generates a new \code{SoftmaxPolicy} object. Arguments are defined in
#' the Argument section above.}
#' }
#'
#' \describe{
#' \item{\code{set_parameters()}}{each policy needs to assign the parameters it wants to keep track of
#' to list \code{self$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]]$parameter_name}
#' }
#' }
#'
#' \describe{
#' \item{\code{get_action(context)}}{
#' here, a policy decides which arm to choose, based on the current values
#' of its parameters and, potentially, the current context.
#' }
#' }
#'
#' \describe{
#' \item{\code{set_reward(reward, context)}}{
#' in \code{set_reward(reward, context)}, a policy updates its parameter values
#' based on the reward received, and, potentially, the current context.
#' }
#' }
#'
#' @references
#'
#' Kuleshov, V., & Precup, D. (2014). Algorithms for multi-armed bandit problems.
#' arXiv preprint arXiv:1402.6028.
#'
#' Cesa-Bianchi, N., Gentile, C., Lugosi, G., & Neu, G. (2017). Boltzmann exploration done right.
#' In Advances in Neural Information Processing Systems (pp. 6284-6293).
#'
#' @seealso
#'
#' Core contextual classes: \code{\link{Bandit}}, \code{\link{Policy}}, \code{\link{Simulator}},
#' \code{\link{Agent}}, \code{\link{History}}, \code{\link{Plot}}
#'
#' Bandit subclass examples: \code{\link{BasicBernoulliBandit}}, \code{\link{ContextualLogitBandit}},
#' \code{\link{OfflineReplayEvaluatorBandit}}
#'
#' Policy subclass examples: \code{\link{EpsilonGreedyPolicy}}, \code{\link{ContextualLinTSPolicy}}
#'
#' @examples
#'
#' horizon <- 100L
#' simulations <- 100L
#' weights <- c(0.9, 0.1, 0.1)
#'
#' policy <- SoftmaxPolicy$new(tau = 0.1)
#' bandit <- BasicBernoulliBandit$new(weights = weights)
#' agent <- Agent$new(policy, bandit)
#'
#' history <- Simulator$new(agent, horizon, simulations, do_parallel = FALSE)$run()
#'
#' plot(history, type = "cumulative")
#'
#' plot(history, type = "arms")
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.