This paper is concerned with identifying the arm with the highest mean in a multi-armed
bandit problem using as few independent samples from the arms as possible. While the so …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in
many different contexts in statistics and machine learning. Whereas the achievable limit in …

We give a complete characterization of the complexity of best-arm identification in one-
parameter bandit problems. We prove a new, tight lower bound on the sample complexity …

Reinforcement learning is a learning paradigm concerned with learning to control a system
so as to maximize a numerical performance measure that expresses a long-term objective …

We study the problem of exploration in stochastic Multi-Armed Bandits. Even in the simplest
setting of identifying the best arm, there remains a logarithmic multiplicative gap between the …

In this paper, we propose new problem-independent lower bounds on the sample
complexity and regret in episodic MDPs, with a particular focus on the\emph {non-stationary …