### Abstract

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-Armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a well-known knapsack problem: find an optimal packing of items into a limited-size knapsack. The BwK problem is a common generalization of numerous motivating examples, which range from dynamic pricing to repeated auctions to dynamic ad allocation to network routing and scheduling. While the prior work on BwK focused on the stochastic version, we pioneer the other extreme in which the outcomes can be chosen adversarially. This is a considerably harder problem, compared to both the stochastic version and the 'classic' adversarial bandits, in that regret minimization is no longer feasible. Instead, the objective is to minimize the competitive ratio: The ratio of the benchmark reward to algorithm's reward. We design an algorithm with competitive ratio O(log T) relative to the best fixed distribution over actions, where T is the time horizon; we also prove a matching lower bound. The key conceptual contribution is a new perspective on the stochastic version of the problem. We suggest a new algorithm for the stochastic version, which builds on the framework of regret minimization in repeated games and admits a substantially simpler analysis compared to prior work. We then analyze this algorithm for the adversarial version, and use it as a subroutine to solve the latter. Our algorithm is the first 'black-box reduction' from bandits to BwK: it takes an arbitrary bandit algorithm and uses it as a subroutine. We use this reduction to derive several extensions.

Original language | English (US) |
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Title of host publication | Proceedings - 2019 IEEE 60th Annual Symposium on Foundations of Computer Science, FOCS 2019 |

Publisher | IEEE Computer Society |

Pages | 202-219 |

Number of pages | 18 |

ISBN (Electronic) | 9781728149523 |

DOIs | |

State | Published - Nov 2019 |

Externally published | Yes |

Event | 60th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2019 - Baltimore, United States Duration: Nov 9 2019 → Nov 12 2019 |

### Publication series

Name | Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS |
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Volume | 2019-November |

ISSN (Print) | 0272-5428 |

### Conference

Conference | 60th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2019 |
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Country | United States |

City | Baltimore |

Period | 11/9/19 → 11/12/19 |

### All Science Journal Classification (ASJC) codes

- Computer Science(all)

### Keywords

- Adversarial Online Learning
- Multi-Armed bandits
- Online Packing

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## Cite this

*Proceedings - 2019 IEEE 60th Annual Symposium on Foundations of Computer Science, FOCS 2019*(pp. 202-219). [8948695] (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/FOCS.2019.00022