My 1000th post. What can I say?! This was by far the most random! Thankyou Kitschnkarma for your suggestion!
adversarial multi-armed bandit problem. In the multi-armed bandit problem, a gambler must decide which arm of K non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the expected per-round payoff of our algorithm approaches that of the best arm at the rate O(T-1/3), and we give an improved rate of convergence when the best arm has fairly low payoff. We also consider a setting in which the player has a team of “experts” advising him on which arm to play; here, we give a strategy that will guarantee expected payoff close to that of the best expert. Finally, we apply our result to the problem of learning to play an unknown repeated matrix game against an all-powerful adversary Are you with me so far!?
For those of you waiting with baited breath, the whole passage goes. . . "Love, courage, the spirit of adventure and the struggle of man against the adversity of life itself, the core values of companionship and fidelity, the will to win through against the battle of the day to day trials and tribulations that beset each and every one." It sounds like owning a vdub to me!
