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Hyper-parameter Tuning for the Contextual Bandit
[article]
2020
arXiv
pre-print
We study here the problem of learning the exploration exploitation trade-off in the contextual bandit problem with linear reward function setting. In the traditional algorithms that solve the contextual bandit problem, the exploration is a parameter that is tuned by the user. However, our proposed algorithm learn to choose the right exploration parameters in an online manner based on the observed context, and the immediate reward received for the chosen action. We have presented here two
arXiv:2005.02209v1
fatcat:fqornlf4t5anvbsqomg5yvci2q