Description Usage Arguments Methods References See Also Examples
RandomPolicy
always explores, choosing arms uniformly at random.
In that respect, RandomPolicy
is the mirror image of a pure greedy policy,
which would always seek to exploit.
1 | policy <- RandomPolicy(name = "RandomPolicy")
|
name
character string specifying this policy. name
is, among others, saved to the History log and displayed in summaries and plots.
new()
Generates a new RandomPolicy
object. Arguments are defined in the Argument
section above.
set_parameters()
each policy needs to assign the parameters it wants to keep track of
to list self$theta_to_arms
that has to be defined in set_parameters()
's body.
The parameters defined here can later be accessed by arm index in the following way:
theta[[index_of_arm]]$parameter_name
get_action(context)
here, a policy decides which arm to choose, based on the current values of its parameters and, potentially, the current context.
set_reward(reward, context)
in set_reward(reward, context)
, a policy updates its parameter values
based on the reward received, and, potentially, the current context.
Gittins, J., Glazebrook, K., & Weber, R. (2011). Multi-armed bandit allocation indices. John Wiley & Sons. (Original work published 1989)
Core contextual classes: Bandit
, Policy
, Simulator
,
Agent
, History
, Plot
Bandit subclass examples: BasicBernoulliBandit
, ContextualLogitBandit
,
OfflineReplayEvaluatorBandit
Policy subclass examples: EpsilonGreedyPolicy
, ContextualLinTSPolicy
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