Results 11 to 20 of about 313 (110)
On Gittins’ index theorem in continuous time
Stochastic Processes and their Applications, 2007 The Gittins' index theorem is proved for dynamic allocation problems of the multi-armed bandit type. The authors formalize their allocation problem as a multiparameter control problem. They construct the new approach to Gittins' index theorem that is based on a characteristic representation property which relates the Gittins index to the accumulated ...
Bank, Peter, Küchler, Christian
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A Sampling-Based Method for Gittins Index Approximation [PDF]
, 2023 A sampling-based method is introduced to approximate the Gittins index for a general family of alternative bandit processes. The approximation consists of a truncation of the optimization horizon and support for the immediate rewards, an optimal stopping value approximation, and a stochastic approximation procedure.
Baas, Stef +2 more
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On the optimality of the Gittins index rule in multi-armed bandits with multiple plays [PDF]
Proceedings of 1995 34th IEEE Conference on Decision and Control, 1999 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pandelis, Dimitrios G. +1 more
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Biometrics, 2021 AbstractThe most common objective for response‐adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data.
Helen Yvette Barnett +3 more
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Covariate-adjusted Response-adaptive Randomization for Multi-arm Clinical Trials Using a Modified Forward Looking Gittins Index Rule [PDF]
Biometrics, 2017 Summary We introduce a non-myopic, covariate-adjusted response adaptive (CARA) allocation design for multi-armed clinical trials. The allocation scheme is a computationally tractable procedure based on the Gittins index solution to the classic multi-armed bandit problem and extends the procedure recently proposed in Villar et al. (2015).
Villar, Sofía S, Rosenberger, William F
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, 2023 Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive.
James K. He +2 more
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IEEE Access, 2018 The problem of rested and restless multi-armed bandits with constrained availability (RMAB-CA) of arms is considered. The states of arms evolve in Markovian manner and the exact states are hidden from the decision maker. First, some structural results on
Varun Mehta +4 more
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A short proof of the Gittins index theorem [PDF]
Proceedings of 32nd IEEE Conference on Decision and Control, 1994 There are several alternative proofs of the Gittins index theorem for the multi-armed bandit problem, and this paper presents yet another proof of the same celebrated result. Unlike previous proofs based on (different) interchange arguments this proof is based on an inductive argument leading to easy calculations.
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Systematic search, belated information, and the gittins' index [PDF]
Economics Letters, 1981 Abstract This paper uses multi-armed bandit methods to characterize the optimal solution of a rather complicated search problem. The job search is conducted systematically and there is belated information, that is, some aspect of the job is discerned only after the job has been tested for one period.
Brian P. McCall, John J. McCall
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Characterization of the Gittins index for sequential multistage jobs
CoRR, 2021 144 pages, no ...
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