RandomPolicy {contextual}R Documentation

Policy: Random


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.


policy <- RandomPolicy(name = "RandomPolicy")



character string specifying this policy. name is, among others, saved to the History log and displayed in summaries and plots.



Generates a new RandomPolicy object. Arguments are defined in the Argument section above.


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


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)

See Also

Core contextual classes: Bandit, Policy, Simulator, Agent, History, Plot

Bandit subclass examples: BasicBernoulliBandit, ContextualLogitBandit, OfflineReplayEvaluatorBandit

Policy subclass examples: EpsilonGreedyPolicy, ContextualLinTSPolicy


horizon            <- 100L
simulations        <- 100L
weights            <- c(0.9, 0.1, 0.1)

policy             <- RandomPolicy$new()
bandit             <- BasicBernoulliBandit$new(weights = weights)
agent              <- Agent$new(policy, bandit)

history            <- Simulator$new(agent, horizon, simulations, do_parallel = FALSE)$run()

plot(history, type = "arms")

[Package contextual version Index]