Noise Free Multi-armed Bandit Game

Mistake Bounds on Noise-Free Multi-Armed Bandit Game

We study the {0, 1}-loss version of adaptive adversarial multi-armed bandit problems with α(≥ 1) lossless arms. For the problem, we show a tight bound K − α − Θ(1/T ) on the minimax expected number of mistakes (1-losses), where K is the number of arms and T is the number of rounds.

A two armed bandit type problem

Multiple Identifications in Multi-Armed Bandits

We study the problem of identifying the top m arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that were previously out of reach. In particular we show that this idea of successive...

The Combinatorial Multi-Armed Bandit Problem and Its Application to Real-Time Strategy Games

Game tree search in games with large branching factors is a notoriously hard problem. In this paper, we address this problem with a new sampling strategy for Monte Carlo Tree Search (MCTS) algorithms, called Naı̈ve Sampling, based on a variant of the Multi-armed Bandit problem called the Combinatorial Multi-armed Bandit (CMAB) problem. We present a new MCTS algorithm based on Naı̈ve Sampling call...

Gap-free Bounds for Stochastic Multi-Armed Bandit

We consider the stochastic multi-armed bandit problem with unknown horizon. We present a randomized decision strategy which is based on updating a probability distribution through a stochastic mirror descent type algorithm. We consider separately two assumptions: nonnegative losses or arbitrary losses with an exponential moment condition. We prove optimal (up to logarithmic factors) gap-free bo...