3,177 research outputs found
Pure Exploration for Multi-Armed Bandit Problems
We consider the framework of stochastic multi-armed bandit problems and study
the possibilities and limitations of forecasters that perform an on-line
exploration of the arms. These forecasters are assessed in terms of their
simple regret, a regret notion that captures the fact that exploration is only
constrained by the number of available rounds (not necessarily known in
advance), in contrast to the case when the cumulative regret is considered and
when exploitation needs to be performed at the same time. We believe that this
performance criterion is suited to situations when the cost of pulling an arm
is expressed in terms of resources rather than rewards. We discuss the links
between the simple and the cumulative regret. One of the main results in the
case of a finite number of arms is a general lower bound on the simple regret
of a forecaster in terms of its cumulative regret: the smaller the latter, the
larger the former. Keeping this result in mind, we then exhibit upper bounds on
the simple regret of some forecasters. The paper ends with a study devoted to
continuous-armed bandit problems; we show that the simple regret can be
minimized with respect to a family of probability distributions if and only if
the cumulative regret can be minimized for it. Based on this equivalence, we
are able to prove that the separable metric spaces are exactly the metric
spaces on which these regrets can be minimized with respect to the family of
all probability distributions with continuous mean-payoff functions
Pure Exploration for Multi-Armed Bandit Problems
We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of forecasters that perform an on-line exploration of the arms. These forecasters are assessed in terms of their simple regret, a regret notion that captures the fact that exploration is only constrained by the number of available rounds (not necessarily known in advance), in contrast to the case when the cumulative regret is considered and when exploitation needs to be performed at the same time. We believe that this performance criterion is suited to situations when the cost of pulling an arm is expressed in terms of resources rather than rewards. We discuss the links between the simple and the cumulative regret. One of the main results in the case of a finite number of arms is a general lower bound on the simple regret of a forecaster in terms of its cumulative regret: the smaller the latter, the larger the former. Keeping this result in mind, we then exhibit upper bounds on the simple regret of some forecasters. The paper ends with a study devoted to continuous-armed bandit problems; we show that the simple regret can be minimized with respect to a family of probability distributions if and only if the cumulative regret can be minimized for it. Based on this equivalence, we are able to prove that the separable metric spaces are exactly the metric spaces on which these regrets can be minimized with respect to the family of all probability distributions with continuous mean-payoff functions
BelMan: Bayesian Bandits on the Belief--Reward Manifold
We propose a generic, Bayesian, information geometric approach to the
exploration--exploitation trade-off in multi-armed bandit problems. Our
approach, BelMan, uniformly supports pure exploration,
exploration--exploitation, and two-phase bandit problems. The knowledge on
bandit arms and their reward distributions is summarised by the barycentre of
the joint distributions of beliefs and rewards of the arms, the
\emph{pseudobelief-reward}, within the beliefs-rewards manifold. BelMan
alternates \emph{information projection} and \emph{reverse information
projection}, i.e., projection of the pseudobelief-reward onto beliefs-rewards
to choose the arm to play, and projection of the resulting beliefs-rewards onto
the pseudobelief-reward. It introduces a mechanism that infuses an exploitative
bias by means of a \emph{focal distribution}, i.e., a reward distribution that
gradually concentrates on higher rewards. Comparative performance evaluation
with state-of-the-art algorithms shows that BelMan is not only competitive but
can also outperform other approaches in specific setups, for instance involving
many arms and continuous rewards.Comment: 36 pages, 14 figures, accepted in ECML PKDD 201
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits
In this paper, we propose an information-theoretic exploration strategy for
stochastic, discrete multi-armed bandits that achieves optimal regret. Our
strategy is based on the value of information criterion. This criterion
measures the trade-off between policy information and obtainable rewards. High
amounts of policy information are associated with exploration-dominant searches
of the space and yield high rewards. Low amounts of policy information favor
the exploitation of existing knowledge. Information, in this criterion, is
quantified by a parameter that can be varied during search. We demonstrate that
a simulated-annealing-like update of this parameter, with a sufficiently fast
cooling schedule, leads to an optimal regret that is logarithmic with respect
to the number of episodes.Comment: Entrop
Pure Exploration with Multiple Correct Answers
We determine the sample complexity of pure exploration bandit problems with
multiple good answers. We derive a lower bound using a new game equilibrium
argument. We show how continuity and convexity properties of single-answer
problems ensures that the Track-and-Stop algorithm has asymptotically optimal
sample complexity. However, that convexity is lost when going to the
multiple-answer setting. We present a new algorithm which extends
Track-and-Stop to the multiple-answer case and has asymptotic sample complexity
matching the lower bound
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