學(xué)術(shù)空間 / 論文 / 會議論文
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
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| 作 者 | Huasen Wu, R. Srikant, Xin Liu, and Chong Jiang |
| 會議名稱 | NIPS'15, available at: http://papers.nips.cc/paper/6008-algorithms-with-logarithmic-or-sublinear-regret-for-constrained-contextual-bandits.pdf Full version available at: http://arxiv.org/abs/1504.06937 |
| 發(fā)表日期 | 2015 年 12 月 |
| 摘 要 |
We study contextual bandits with budget and time constraints, referred to as constrained contextual bandits.The time and budget constraints significantly complicate the exploration and exploitation tradeoff because they introduce complex coupling among contexts over time.Such coupling effects make it difficult to obtain oracle solutions that assume known statistics of bandits. To gain insight, we first study unit-cost systems with known context distribution. When the expected rewards are known, we develop an approximation of the oracle, referred to Adaptive-Linear-Programming (ALP), which achieves near-optimality and only requires the ordering of expected rewards. With these highly desirable features, we then combine ALP with the upper-confidence-bound (UCB) method in the general case where the expected rewards are unknown {\it a priori}. We show that the proposed UCB-ALP algorithm achieves logarithmic regret except for certain boundary cases. Further, we design algorithms and obtain similar regret analysis results for more general systems with unknown context distribution and heterogeneous costs. To the best of our knowledge, this is the first work that shows how to achieve logarithmic regret in constrained contextual bandits. |
| 關(guān) 鍵 字 | Constrained contextual bandit, upper confidential bound (UCB) |
| 附 件 |
