Demo: Basic Epsilon Greedy"
In contextual: Simulation and Analysis of Contextual Multi-Armed Bandit Policies

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(contextual)

policy             <- EpsilonGreedyPolicy$new(epsilon = 0.1)

bandit             <- BasicBernoulliBandit$new(weights = c(0.6, 0.1, 0.1))
agent              <- Agent$new(policy,bandit)

simulator          <- Simulator$new(agents      = agent,
                                    horizon     = 100,
                                    simulations = 1000)

history            <- simulator$run()

plot(history, type = "cumulative", regret = TRUE, disp = "ci",
              traces_max = 100, traces_alpha = 0.1, traces = TRUE)

summary(history)

Agents:

  EpsilonGreedy

Cumulative regret:

         agent   t sims cum_regret cum_regret_var cum_regret_sd
 EpsilonGreedy 100 1000      8.951       116.7133      10.80339


Cumulative reward:

         agent   t sims cum_reward cum_reward_var cum_reward_sd
 EpsilonGreedy 100 1000      51.09       141.6215      11.90048


Cumulative reward rate:

         agent   t sims cur_reward cur_reward_var cur_reward_sd
 EpsilonGreedy 100 1000     0.5109       1.416215     0.1190048