Description Details Usage Arguments Methods See Also Examples
Contextfree Gaussian multiarmed bandit.
Simulates k
Gaussian arms where each arm models the reward as a normal
distribution with provided mean mu
and standard deviation sigma
.
1  bandit < BasicGaussianBandit$new(mu_per_arm, sigma_per_arm)

mu_per_arm
numeric vector; mean mu
for each of the bandit's k
arms
sigma_per_arm
numeric vector; standard deviation of additive Gaussian noise for each of
the bandit's k
arms. Set to zero for no noise.
new(mu_per_arm, sigma_per_arm)
generates and instantializes a
new BasicGaussianBandit
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
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