Using the epsilon-greedy strategy to generate the n-armed bandit testbed.

n_armed_testbed.m

function [] = n_armed_testbed(nB,nA,nP,sigma)
% 
% Generates the 10-armed bandit testbed.
% 
% Inputs: 
%   nB: the number of bandits
%   nA: the number of arms
%   nP: the number of plays (times we will pull a arm)
%   sigma: the standard deviation of the return from each of the arms
% 
% Written by:
% -- 
% John L. Weatherwax                2007-11-13
% 
% email: [email protected]
% 
% Please send comments and especially bug reports to the
% above email address.
% 
%-----

%close all; 
%clc; 
%clear; 

if( nargin<1 ) % the number of bandits: 
  nB = 2000;  
end
if( nargin<2 ) % the number of arms: 
  nA = 10; 
end
if( nargin<3 ) % the number of plays (times we will pull a arm):
  nP = 10000; 
end
if( nargin<4 ) % the standard deviation of the return from each of the arms: 
  sigma = 1.0; 
end

randn('seed',0); 

% generate the TRUE reward Q^{\star}: 
qStarMeans = mvnrnd( zeros(nB,nA), eye(nA) ); 

% run an experiment for each epsilon:
% 0 => fully greedy
% 1 => explore on each trial
epsArray = [ 0, 0.01, 0.1 ]; %, 1 ]; 

% assume we have at least ONE draw from each "arm" (initialize with use the qStarMeans matrix):
qT0 = mvnrnd( qStarMeans, eye(nA) );

avgReward    = zeros(length(epsArray),nP); 
perOptAction = zeros(length(epsArray),nP); 
cumReward    = zeros(length(epsArray),nP); 
cumProb      = zeros(length(epsArray),nP); 
for ei=1:length(epsArray), 
  tEps = epsArray(ei); 

  %qT = qT0;  % <- initialize to one draw per arm 
  qT = zeros(size(qT0));  % <- initialize to zero draws per arm (no knowledge)
  qN = ones( nB, nA ); % keep track of the number draws on this arm 
  qS = qT;             % keep track of the SUM of the rewards (qT = qS./qN) 

  allRewards      = zeros(nB,nP); 
  pickedMaxAction = zeros(nB,nP); 
  for bi=1:nB, % pick a bandit
    for pi=1:nP, % make a play
      % determine if this move is exploritory or greedy: 
      if( rand(1) <= tEps ) % pick a RANDOM arm: 
        [dum,arm] = histc(rand(1),linspace(0,1+eps,nA+1)); clear dum; 
      else                  % pick the GREEDY arm:
        [dum,arm] = max( qT(bi,:) ); clear dum; 
      end
      % determine if the arm selected is the best possible: 
      [dum,bestArm] = max( qStarMeans(bi,:) ); 
      if( arm==bestArm ) pickedMaxAction(bi,pi) = 1; end
      % get the reward from drawing on that arm: 
      reward = qStarMeans(bi,arm) + sigma*randn(1); %introduce some noise, or the pure greedy approach would be the best
      allRewards(bi,pi) = reward; 
      % update qN,qS,qT: 
      qN(bi,arm) = qN(bi,arm)+1;
      qS(bi,arm) = qS(bi,arm)+reward; 
      qT(bi,arm) = qS(bi,arm)/qN(bi,arm); 
    end
  end

  avgRew          = mean(allRewards,1);
  avgReward(ei,:) = avgRew(:).'; 
  percentOptAction   = mean(pickedMaxAction,1);
  perOptAction(ei,:) = percentOptAction(:).';
  csAR            = cumsum(allRewards,2); % do a cummulative sum across plays for each bandit
  csRew           = mean(csAR,1);
  cumReward(ei,:) = csRew(:).';
  csPA          = cumsum(pickedMaxAction,2)./cumsum(ones(size(pickedMaxAction)),2);
  csProb        = mean(csPA,1);
  cumProb(ei,:) = csProb(:).';
end

% produce the average rewards plot: 
% 
figure; hold on; clrStr = 'brkc'; all_hnds = []; 
for ei=1:length(epsArray),
  %all_hnds(ei) = plot( [ 0, avgReward(ei,:) ], [clrStr(ei)] ); 
  all_hnds(ei) = plot( 1:nP, avgReward(ei,:), [clrStr(ei),'-'] ); 
end 
legend( all_hnds, { '0', '0.01', '0.1' }, 'Location', 'SouthEast' ); 
axis tight; grid on; 
xlabel( 'plays' ); ylabel( 'Average Reward' ); 

% produce the percent optimal action plot: 
% 
figure; hold on; clrStr = 'brkc'; all_hnds = []; 
for ei=1:length(epsArray),
  %all_hnds(ei) = plot( [ 0, avgReward(ei,:) ], [clrStr(ei)] ); 
  all_hnds(ei) = plot( 1:nP, perOptAction(ei,:), [clrStr(ei),'-'] ); 
end 
legend( all_hnds, { '0', '0.01', '0.1' }, 'Location', 'SouthEast' ); 
axis( [ 0, nP, 0, 1 ] ); axis tight; grid on; 
xlabel( 'plays' ); ylabel( '% Optimal Action' );

% produce the cummulative average rewards plot: 
% 
figure; hold on; clrStr = 'brkc'; all_hnds = []; 
for ei=1:length(epsArray),
  all_hnds(ei) = plot( 1:nP, cumReward(ei,:), [clrStr(ei),'-'] ); 
end 
legend( all_hnds, { '0', '0.01', '0.1' }, 'Location', 'SouthEast' ); 
axis tight; grid on; 
xlabel( 'plays' ); ylabel( 'Cummulative Average Reward' ); 

% produce the cummulative percent optimal action plot: 
% 
figure; hold on; clrStr = 'brkc'; all_hnds = []; 
for ei=1:length(epsArray),
  all_hnds(ei) = plot( 1:nP, cumProb(ei,:), [clrStr(ei),'-'] ); 
end 
legend( all_hnds, { '0', '0.01', '0.1' }, 'Location', 'SouthEast' ); 
axis( [ 0, nP, 0, 1 ] ); axis tight; grid on; 
xlabel( 'plays' ); ylabel( 'Cummulative % Optimal Action' );

Some good ways to plot graphs with legends