contextual (version 0.9.8.4)

ContinuumBandit: Bandit: ContinuumBandit

Description

A function based continuum multi-armed bandit where arms are chosen from a subset of the real line and the mean rewards are assumed to be a continuous function of the arms.

Usage

   bandit <- ContinuumBandit$new(FUN)

Arguments

FUN

continuous function.

Methods

new(FUN)

generates and instantializes a new ContinuumBandit instance.

get_context(t)

argument:

• t: integer, time step t.

returns a named list containing the current d x k dimensional matrix context$X, the number of arms context$k and the number of features context$d.

get_reward(t, context, action)

arguments:

• t: integer, time step t.

• context: list, containing the current context$X (d x k context matrix), context$k (number of arms) and context$d (number of context features) (as set by bandit).

• action: list, containing action$choice (as set by policy).

returns a named list containing reward$reward and, where computable, reward$optimal (used by "oracle" policies and to calculate regret).

See Also

Core contextual classes: Bandit, Policy, Simulator, Agent, History, Plot

Bandit subclass examples: BasicBernoulliBandit, ContextualLogitBandit, OfflineReplayEvaluatorBandit

Policy subclass examples: EpsilonGreedyPolicy, ContextualLinTSPolicy

Examples

# NOT RUN {
horizon            <- 1500
simulations        <- 100

continuous_arms  <- function(x) {
  -0.1*(x - 5) ^ 2 + 3.5  + rnorm(length(x),0,0.4)
}

int_time    <- 100
amplitude   <- 0.2
learn_rate  <- 0.3
omega       <- 2*pi/int_time
x0_start    <- 2.0

policy             <- LifPolicy$new(int_time, amplitude, learn_rate, omega, x0_start)

bandit             <- ContinuumBandit$new(FUN = continuous_arms)

agent              <- Agent$new(policy,bandit)

history            <- Simulator$new(     agents = agent,
                                         horizon = horizon,
                                         simulations = simulations,
                                         save_theta = TRUE             )$run()

plot(history, type = "average", regret = FALSE)
# }