• Corpus ID: 245502950

The Statistical Complexity of Interactive Decision Making

@article{Foster2021TheSC,
  title={The Statistical Complexity of Interactive Decision Making},
  author={Dylan J. Foster and Sham M. Kakade and Jian Qian and Alexander Rakhlin},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.13487}
}
A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning algorithms that achieve near-optimal regret. This question is analogous to the classical problem of optimal (supervised) statistical learning, where there are well-known complexity measures (e.g., VC dimension and Rademacher complexity) that govern the statistical complexity of learning. However, characterizing the… 

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