New files for other algorithms

epsilon_greedy_starter.jl

@@ -52,7 +52,7 @@ for b in bandits
     println("Mean estimate: ", b.p_estimate)
 end
 
-println("Total reward eaarned: ", sum(rewards))
+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)

optimistic_starter.jl

@@ -0,0 +1,51 @@
+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,5,1)
+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 
+    j = argmax([b.p_estimate for b in bandits])
+    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 of times selected each bandit ", [b.N for b in bandits])
+
+cumulative_rewards = cumsum(rewards)
+win_rates = cumulative_rewards ./ Array(1:num_trials)
+plot(win_rates) 
+plot!(ones(num_trials) .* max(bandit_probs...))

ubc1.jl

@@ -0,0 +1,50 @@
+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
+
+function ucb(mean::Number,n::Number,nⱼ::Number)
+    return mean + √(2*log(n) / nⱼ)
+end
+
+num_trials = 100000;
+ϵ = 0.1;
+bandit_probs = [0.2,0.5,0.75];
+
+bandits = [Bandit(p) for p in bandit_probs];
+rewards = zeros(num_trials);
+total_plays = 0;
+optimal_j = argmax([b.p for b in bandits]);
+
+for j in 1:size(bandits)[1]
+    x = pull(bandits[j])
+    total_plays += 1
+    update(bandits[j],x)
+end
+    
+for i in 1:num_trials 
+    j = argmax([ucb(b.p_estimate,total_plays,b.N) for b in bandits])
+    x = pull(bandits[j])
+    total_plays += 1
+    update(bandits[j],x)
+
+    rewards[i] = x
+end
+
+cumulative_average = cumsum(rewards) ./ Array(1:num_trials);
+plot(cumulative_average,xaxis=:log) 
+plot!(ones(num_trials) .* max(bandit_probs...))