reinforcement/epsilon_greedy_starter.jl

using Random
using Plots

mutable struct Bandit
    p::Number #the win rate
    p_estimate::Number #How to estimate the win rate
    N::Number #Number of samples

    Bandit(p) = new(p,0,0)
end

function pull(ban::Bandit)
   return convert(Int,rand() < ban.p) 
end

function update(ban::Bandit, x::Number) #x is a sample number
    ban.N += 1
    ban.p_estimate = ((ban.N - 1) * ban.p_estimate + x) / ban.N
end

num_trials = 10000
ϵ = 0.1
bandit_probs = [0.2,0.5,0.75]

bandits = [Bandit(p) for p in bandit_probs]
rewards = zeros(num_trials)
num_times_explored = 0
num_times_exploited = 0
num_optimal = 0
optimal_j = argmax([b.p for b in bandits])
println("Optimal j: ", optimal_j)
    
for i in 1:num_trials 
    if rand() < ϵ
        num_times_explored += 1
        j = rand(1:size(bandits)[1])
    else
        num_times_exploited += 1
        j = argmax([b.p_estimate for b in bandits])
    end

    if j == optimal_j
        num_optimal += 1
    end

    x = pull(bandits[j])
    rewards[i] = x
    update(bandits[j],x)
end

for b in bandits
    println("Mean estimate: ", b.p_estimate)
end

println("Total reward earned: ", sum(rewards))
println("Overall win rate: ", sum(rewards)/ num_trials)
println("Number times explored: ", num_times_explored)
println("Number times exploited: ", num_times_exploited)
println("Number of times the optimal bandit was selected: ", num_optimal)

cumulative_rewards = cumsum(rewards)
win_rates = cumulative_rewards ./ Array(1:num_trials)
plot(win_rates) 
plot!(ones(num_trials) .* max(bandit_probs...))