Smooth Sequential Optimisation with Delayed Feedback
- URL: http://arxiv.org/abs/2106.11294v2
- Date: Tue, 22 Jun 2021 06:35:19 GMT
- Title: Smooth Sequential Optimisation with Delayed Feedback
- Abstract summary: We propose a novel adaptation to shrinkage that estimates smoothed reward estimates from cumulative inputs.
Using numerical simulations, we show that this adaptation retains the benefits of shrinkage, and improves the stability of reward estimation by more than 50%.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Stochastic delays in feedback lead to unstable sequential learning using
multi-armed bandits. Recently, empirical Bayesian shrinkage has been shown to
improve reward estimation in bandit learning. Here, we propose a novel
adaptation to shrinkage that estimates smoothed reward estimates from windowed
cumulative inputs, to deal with incomplete knowledge from delayed feedback and
non-stationary rewards. Using numerical simulations, we show that this
adaptation retains the benefits of shrinkage, and improves the stability of
reward estimation by more than 50%. Our proposal reduces variability in
treatment allocations to the best arm by up to 3.8x, and improves statistical
accuracy - with up to 8% improvement in true positive rates and 37% reduction
in false positive rates. Together, these advantages enable control of the
trade-off between speed and stability of adaptation, and facilitate
human-in-the-loop sequential optimisation.
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