Description Details Usage Arguments Methods See Also Examples
Samples data from a basic logistic regression model.
ContextualLogitBandit linear predictors are generated from the dot product of a random d
dimensional
normal weight vector and uniform random d x k
dimensional context matrices with equal weights per
arm. This product is then inverse-logit transformed to generate k
dimensional binary (0/1) reward
vectors by randomly sampling from a Bernoulli distribution.
1 | bandit <- ContextualLogitBandit$new(k, d, intercept = TRUE)
|
k
integer; number of bandit arms
d
integer; number of contextual features
intercept
logical; if TRUE (default) it adds a constant (1.0) dimension to each context X at the end.
new(k, d, intercept = TRUE)
generates and instantializes a new
ContextualLogitBandit
instance.
get_context(t)
argument:
t
: integer, time step t
.
returns a named list
containing the current d x k
dimensional matrix context$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).
post_initialization()
initializes d x k
beta matrix.
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 | ## Not run:
horizon <- 800L
simulations <- 30L
bandit <- ContextualLogitBandit$new(k = 5, d = 5, intercept = TRUE)
agents <- list(Agent$new(ContextualLinTSPolicy$new(0.1), bandit),
Agent$new(EpsilonGreedyPolicy$new(0.1), bandit),
Agent$new(LinUCBGeneralPolicy$new(0.6), bandit),
Agent$new(ContextualEpochGreedyPolicy$new(8), bandit),
Agent$new(LinUCBHybridOptimizedPolicy$new(0.6), bandit),
Agent$new(LinUCBDisjointOptimizedPolicy$new(0.6), bandit))
simulation <- Simulator$new(agents, horizon, simulations)
history <- simulation$run()
plot(history, type = "cumulative", regret = FALSE,
rate = TRUE, legend_position = "right")
## End(Not run)
|
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