Description Usage Arguments Methods See Also Examples
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.
1 | bandit <- ContinuumBandit$new(FUN)
|
continuous function.
new(FUN)
generates and instantializes a new ContinuumBandit
instance.
get_context(t)
argument:
t
: integer, time step t
.
returns a named list
containing the current d x k
dimensional matrix context$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).
Core contextual classes: Bandit
, Policy
, Simulator
,
Agent
, History
, Plot
Bandit subclass examples: BasicBernoulliBandit
, ContextualLogitBandit
,
OfflineReplayEvaluatorBandit
Policy subclass examples: EpsilonGreedyPolicy
, ContextualLinTSPolicy
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ## 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)
## End(Not run)
|
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