OraclePolicy is a also known as a "cheating" or "godlike"
policy, as it knows the reward probabilities at all times,
and will always play the optimal arm. It is often used as
a baseline to compare other policies to.
policy <- OraclePolicy()
namecharacter string specifying this policy. name
is, among others, saved to the History log and displayed in summaries and plots.
new()Generates a new OraclePolicy 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