Nothing
#' @export
EpsilonFirstPolicy <- R6::R6Class(
portable = FALSE,
class = FALSE,
inherit = Policy,
public = list(
first = NULL,
class_name = "EpsilonFirstPolicy",
initialize = function(epsilon = 0.1, N = 1000, time_steps = NULL) {
super$initialize()
self$first <- ceiling(epsilon*N)
if (!is.null(time_steps)) self$first <- time_steps
},
set_parameters = function(context_params) {
self$theta_to_arms <- list('n' = 0, 'mean' = 0)
},
get_action = function(t, context) {
if (sum_of(self$theta$n) < self$first) {
action$choice <- sample.int(context$k, 1, replace = TRUE)
action$propensity <- (1/context$k)
} else {
action$choice <- which_max_list(self$theta$mean)
action$propensity <- 1
}
action
},
set_reward = function(t, context, action, reward) {
arm <- action$choice
reward <- reward$reward
inc(self$theta$n[[arm]]) <- 1
if (sum_of(self$theta$n) < self$first - 1) {
inc(self$theta$mean[[arm]]) <- (reward - self$theta$mean[[arm]]) / self$theta$n[[arm]]
}
self$theta
}
)
)
#' Policy: Epsilon First
#'
#' \code{EpsilonFirstPolicy} implements a "naive" policy where a pure exploration phase
#' is followed by a pure exploitation phase.
#'
#' Exploration happens within the first \code{epsilon * N} time steps.
#' During this time, at each time step \code{t}, \code{EpsilonFirstPolicy} selects an arm at random.
#'
#' Exploitation happens in the following \code{(1-epsilon) * N} steps,
#' selecting the best arm up until \code{epsilon * N} for either the remaining N trials or horizon T.
#'
#' In case of a tie in the exploitation phase, \code{EpsilonFirstPolicy} randomly selects and arm.
#'
#' @name EpsilonFirstPolicy
#'
#'
#' @section Usage:
#' \preformatted{
#' policy <- EpsilonFirstPolicy(epsilon = 0.1, N = 1000, time_steps = NULL)
#' }
#'
#' @section Arguments:
#'
#' \describe{
#' \item{\code{epsilon}}{
#' numeric; value in the closed interval \code{(0,1]} that sets the number of time steps to explore
#' through \code{epsilon * N}.
#' }
#' \item{\code{N}}{
#' integer; positive integer which sets the number of time steps to explore through \code{epsilon * N}.
#' }
#' \item{\code{time_steps}}{
#' integer; positive integer which sets the number of time steps to explore - can be used instead of
#' epsilon and N.
#' }
#' }
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{new(epsilon = 0.1, N = 1000, time_steps = NULL)}}{ Generates a new \code{EpsilonFirstPolicy}
#' object. Arguments are defined in the Argument section above.}
#' }
#'
#' \describe{
#' \item{\code{set_parameters()}}{each policy needs to assign the parameters it wants to keep track of
#' to list \code{self$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]]$parameter_name}
#' }
#' }
#'
#' \describe{
#' \item{\code{get_action(context)}}{
#' here, a policy decides which arm to choose, based on the current values
#' of its parameters and, potentially, the current context.
#' }
#' }
#'
#' \describe{
#' \item{\code{set_reward(reward, context)}}{
#' in \code{set_reward(reward, context)}, a policy updates its parameter values
#' based on the reward received, and, potentially, the current context.
#' }
#' }
#'
#' @references
#'
#' Gittins, J., Glazebrook, K., & Weber, R. (2011). Multi-armed bandit allocation indices. John Wiley & Sons.
#' (Original work published 1989)
#'
#' Sutton, R. S. (1996). Generalization in reinforcement learning: Successful examples using sparse coarse
#' coding. In Advances in neural information processing systems (pp. 1038-1044).
#'
#' Strehl, A., & Littman, M. (2004). Exploration via model based interval estimation. In International
#' Conference on Machine Learning, number Icml.
#'
#' @seealso
#'
#' Core contextual classes: \code{\link{Bandit}}, \code{\link{Policy}}, \code{\link{Simulator}},
#' \code{\link{Agent}}, \code{\link{History}}, \code{\link{Plot}}
#'
#' Bandit subclass examples: \code{\link{BasicBernoulliBandit}}, \code{\link{ContextualLogitBandit}},
#' \code{\link{OfflineReplayEvaluatorBandit}}
#'
#' Policy subclass examples: \code{\link{EpsilonGreedyPolicy}}, \code{\link{ContextualLinTSPolicy}}
#'
#' @examples
#'
#' horizon <- 100L
#' simulations <- 100L
#' weights <- c(0.9, 0.1, 0.1)
#'
#' policy <- EpsilonFirstPolicy$new(time_steps = 50)
#' 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
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