Policy: Softmax

SoftmaxPolicy is very similar to Exp3Policy, but selects an arm based on the probability from the Boltmann distribution. It makes use of a temperature parameter tau, which specifies how many arms we can explore. When tau is high, all arms are explored equally, when tau is low, arms offering higher rewards will be chosen.

Usage

policy <- SoftmaxPolicy(tau = 0.1)

Arguments

tau = 0.1

double, temperature parameter tau specifies how many arms we can explore. When tau is high, all arms are explored equally, when tau is low, arms offering higher rewards will be chosen.

Methods

new(epsilon = 0.1)

Generates a new SoftmaxPolicy 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.

References

Kuleshov, V., & Precup, D. (2014). Algorithms for multi-armed bandit problems. arXiv preprint arXiv:1402.6028.

Cesa-Bianchi, N., Gentile, C., Lugosi, G., & Neu, G. (2017). Boltzmann exploration done right. In Advances in Neural Information Processing Systems (pp. 6284-6293).

See also

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

Bandit subclass examples: BasicBernoulliBandit, ContextualLogitBandit, OfflineReplayEvaluatorBandit

Policy subclass examples: EpsilonGreedyPolicy, ContextualLinTSPolicy

Examples

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

policy             <- SoftmaxPolicy$new(tau = 0.1)
bandit             <- BasicBernoulliBandit$new(weights = weights)
agent              <- Agent$new(policy, bandit)

history            <- Simulator$new(agent, horizon, simulations, do_parallel = FALSE)$run()
#> Simulation horizon: 100
#> Number of simulations: 100
#> Number of batches: 1
#> Starting main loop.
#> Finished main loop.
#> Completed simulation in 0:00:00.950
#> Computing statistics.

plot(history, type = "cumulative")

plot(history, type = "arms")