Description Details Usage Arguments Methods See Also Examples
Contextfree Bernoulli or Binary multiarmed bandit.
Simulates k
Bernoulli arms where each arm issues a reward of one with
uniform probability p
, and otherwise a reward of zero.
In a bandit scenario, this can be used to simulate a hit or miss event, such as if a user clicks on a headline, ad, or recommended product.
1 
weights
numeric vector; probability of reward values for each of the bandit's k
arms
new(weights)
generates and instantializes a new BasicBernoulliBandit
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$optimal
(used by "oracle" policies and to calculate regret).
Core contextual classes: Bandit
, Policy
, Simulator
,
Agent
, History
, Plot
Bandit subclass examples: BasicBernoulliBandit
, ContextualLogitBandit
,
OfflineReplayEvaluatorBandit
Policy subclass examples: EpsilonGreedyPolicy
, ContextualLinTSPolicy
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## Not run:
horizon < 100
sims < 100
policy < EpsilonGreedyPolicy$new(epsilon = 0.1)
bandit < BasicBernoulliBandit$new(weights = c(0.6, 0.1, 0.1))
agent < Agent$new(policy,bandit)
history < Simulator$new(agent, horizon, sims)$run()
plot(history, type = "cumulative", regret = TRUE)
## End(Not run)

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