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CORR
2012
Springer
216views Education» more  CORR 2012»
10 years 7 months ago
Fractional Moments on Bandit Problems
Reinforcement learning addresses the dilemma between exploration to find profitable actions and exploitation to act according to the best observations already made. Bandit proble...
Ananda Narayanan B., Balaraman Ravindran
ALT
2011
Springer
10 years 11 months ago
Deviations of Stochastic Bandit Regret
This paper studies the deviations of the regret in a stochastic multi-armed bandit problem. When the total number of plays n is known beforehand by the agent, Audibert et al. (2009...
Antoine Salomon, Jean-Yves Audibert
AIPS
2011
11 years 3 months ago
Sample-Based Planning for Continuous Action Markov Decision Processes
In this paper, we present a new algorithm that integrates recent advances in solving continuous bandit problems with sample-based rollout methods for planning in Markov Decision P...
Christopher R. Mansley, Ari Weinstein, Michael L. ...
COGSR
2011
71views more  COGSR 2011»
11 years 6 months ago
Psychological models of human and optimal performance in bandit problems
In bandit problems, a decision-maker must choose between a set of alternatives, each of which has a fixed but unknown rate of reward, to maximize their total number of rewards ov...
Michael D. Lee, Shunan Zhang, Miles Munro, Mark St...
COLT
2010
Springer
11 years 9 months ago
Nonparametric Bandits with Covariates
We consider a bandit problem which involves sequential sampling from two populations (arms). Each arm produces a noisy reward realization which depends on an observable random cov...
Philippe Rigollet, Assaf Zeevi
CDC
2008
IEEE
104views Control Systems» more  CDC 2008»
12 years 6 months ago
A structured multiarmed bandit problem and the greedy policy
—We consider a multiarmed bandit problem where the expected reward of each arm is a linear function of an unknown scalar with a prior distribution. The objective is to choose a s...
Adam J. Mersereau, Paat Rusmevichientong, John N. ...

Publication
222views
12 years 8 months ago
Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration
Abstract: Several approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervis...
Christos Dimitrakakis, Michail G. Lagoudakis

Publication
334views
12 years 8 months ago
Rollout Sampling Approximate Policy Iteration
Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schem...
Christos Dimitrakakis, Michail G. Lagoudakis
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