The budgeted multi-armed bandit problem

被引：13
|
作者：
Madani, O
Lizotte, DJ
Greiner, R
机构：
[1] Yahoo, Res Labs, Pasadena, CA 91101 USA
[2] Univ Alberta, Dept Comp Sci, Edmonton, AB T6J 2E8, Canada
来源：
LEARNING THEORY, PROCEEDINGS | 2004年 / 3120卷
关键词：
D O I：
10.1007/978-3-540-27819-1_46
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
引用
收藏
页码：643 / 645
页数：3
相关论文
共 50 条
  • [41] Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates
    Yang, YH
    Zhu, D
    ANNALS OF STATISTICS, 2002, 30 (01): : 100 - 121
  • [42] THE MYOPIC SOLUTION OF THE MULTI-ARMED BANDIT COMPRESSIVE SPECTRUM SENSING PROBLEM
    Bagheri, Saeed
    Scaglione, Anna
    2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [43] Robust Trajectory Selection for Rearrangement Planning as a Multi-Armed Bandit Problem
    Koval, Michael C.
    King, Jennifer E.
    Pollard, Nancy S.
    Srinivasa, Siddhartha S.
    2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2015, : 2678 - 2685
  • [44] The Restless Multi-Armed Bandit Formulation of the Cognitive Compressive Sensing Problem
    Bagheri, Saeed
    Scaglione, Anna
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (05) : 1183 - 1198
  • [45] Dynamic Multi-Armed Bandit Algorithm for the Cyclic Bandwidth Sum Problem
    Rodriguez-Tello, Eduardo
    Narvaez-Teran, Valentina
    Lardeux, Frederic
    IEEE ACCESS, 2019, 7 : 40258 - 40270
  • [46] Multi-armed Bandit Formulation of the Task Partitioning Problem in Swarm Robotics
    Pini, Giovanni
    Brutschy, Arne
    Francesca, Gianpiero
    Dorigo, Marco
    Birattari, Mauro
    SWARM INTELLIGENCE (ANTS 2012), 2012, 7461 : 109 - 120
  • [47] A non-parametric solution to the multi-armed bandit problem with covariates
    Ai, Mingyao
    Huang, Yimin
    Yu, Jun
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2021, 211 : 402 - 413
  • [48] Heterogeneous Stochastic Interactions for Multiple Agents in a Multi-armed Bandit Problem
    Madhushani, Udari
    Leonard, Naomi Ehrich
    2019 18TH EUROPEAN CONTROL CONFERENCE (ECC), 2019, : 3502 - 3507
  • [49] Lower bounds on the sample complexity of exploration in the multi-armed bandit problem
    Mannor, S
    Tsitsiklis, JN
    LEARNING THEORY AND KERNEL MACHINES, 2003, 2777 : 418 - 432
  • [50] Multi-armed bandit algorithms and empirical evaluation
    Vermorel, J
    Mohri, M
    MACHINE LEARNING: ECML 2005, PROCEEDINGS, 2005, 3720 : 437 - 448
12345