[PDF] Top 20 Towards an Improved Strategy for Solving Multi Armed Bandit Problem
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Towards an Improved Strategy for Solving Multi Armed Bandit Problem
... Abstract: Multi-Armed Bandit (MAB) problem is one of the classical reinforcements learning problems that describe the friction between the agent’s exploration and ...an improved reward ... See full document
5
Algorithms for the multi-armed bandit problem
... of bandit characteristics are important for algorithm evaluation The relative performance of algorithms appears to be affected only by the number of arms and the reward ...obtaining improved regret bounds ... See full document
32
The Non-stationary Stochastic Multi-armed Bandit Problem
... stochastic multi-armed bandit with K arms where the rewards are not assumed to be identically distributed, but are generated by a non-stationary stochastic ...target problem- dependent bounds, ... See full document
21
Cooperation control in Parallel SAT Solving: a Multi-armed Bandit Approach
... be improved through controlling dynamically the amount of information shared by the cores[ 11 ], specifically the allowed length of the shared ...as Bandit Ensemble for parallel SAT Solving (BESS), ... See full document
19
Multi-armed Bandit Problems with History
... This problem is meaningful only for the case of stochastic arms [8, 5], since no amount of historic data can help in the adversarial setting ...this problem has not been studied in the ...on bandit ... See full document
11
Training a Quantum Neural Network to Solve the Contextual Multi Armed Bandit Problem
... Contextual Multi-Armed Bandit Problem The multi-armed bandit problem can be described using this ...a strategy to maximize our cumulative ...contextual ... See full document
11
Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem
... general problem of ...learning problem with perfect information, commonly addressed by prediction with expert advice (see, ...related problem is the one of regret against the best strategy ... See full document
21
CiteSeerX — An asymptotically optimal algorithm for the max k-armed bandit problem
... This result leads to a strategy for solving the problem that is asymptotically optimal in the following sense: the gap between the expected maximum payoff obtained by using our strategy [r] ... See full document
8
Slow Fading Channel Selection: A Restless Multi-Armed Bandit Formulation
... a multi-access wireless network in which transmitters dynamically select a frequency band to communicate ...selection problem as a restless multi-armed bandit problem and we ... See full document
5
A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation
... Another trend of research has been towards designing new pre- diction models. The typical approach is to use side information to build a prediction model [1], specially using social informa- tion. For instance, ... See full document
10
Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach
... a problem of Partially Observable Multi-sensor Sequential Change Detec- tion (POMSCD), where only a subset of sensors can be ob- served to monitor a target system for change-point detection at each online ... See full document
8
More specifically, we design a learning algo- rithm that connects active learning with the well-known multi-armed bandit problem
... Copyright c 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Consider the scenario when we were children. We were not commanded to ask questions based on a single ... See full document
7
Approximations of the Restless Bandit Problem
... the multi-armed bandit problem with strongly dependent pay-offs at its full generality is beyond the scope of this paper, we provide a complementary example for this ...the bandit arms ... See full document
37
Scalable Discrete Sampling as a Multi-Armed Bandit Problem
... 3. Algorithms for MABs with a Finite Population and Fixed Confidence The key difference of our problem from the regular MABs is that our rewards are generated from a finite population while regular MABs assume ... See full document
17
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
... Lower bounds for different variants of the multi-armed bandit have been studied by several authors. For the expected regret model, where the regret is defined as the difference between the ideal ... See full document
26
Monotone multi-armed bandit allocations
... The problem has been resolved for stochastic rewards in the strongest possible sense: there exists an ex-post monotone MAB allocation rule whose regret is essentially optimal among all MAB allocation rules ... See full document
5
Mechanisms with learning for stochastic multi armed bandit problems
... allocation strategy to achieve sub-linear ...simple strategy also known as exploration separated algorithms where all the arms are explored for some number of rounds and then the best arm based on the ... See full document
44
Optimizing Adaptive Marketing Experiments with the Multi-Armed Bandit
... the Multi-Armed Bandit Abstract Sequential decision making is central to a range of marketing ...the multi-armed bandit, the conceptual and methodological backbone of this ... See full document
148
On-Line Adaptation of Exploration in the One-Armed Bandit with Covariates Problem
... one- armed bandit with covariates problem, which demonstrate the effectiveness of -ADAPT to correctly control the amount of exploration in finite-time problems and yield rewards that are close to ... See full document
6
A multi-armed bandit approach for exploring partially observed networks
... a multi-armed bandit based exploration algorithm for par- tially observed incomplete ...nonparametric multi-armed bandit algorithm iKNN-UCB with sublinear ...Exploring ... See full document
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