An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives

Shipra Agrawal, Nikhil R. Devanur, Lihong Li
; 29th Annual Conference on Learning Theory, PMLR 49:4-18, 2016.

Abstract

We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be below some pre-fixed budget. The learning agent competes with an arbitrary set of context-dependent policies. This problem was introduced by Badanidiyuru et al., who gave a computationally inefficient algorithm with near-optimal regret bounds for it. We give a \emphcomputationally efficient algorithm for this problem with slightly better regret bounds, by generalizing the approach of Dudik et al. for the non-constrained version of the problem. The computational time of our algorithm scales \emphlogarithmically in the size of the policy space. This answers the main open question of Badanidiyuru et al. We also extend our results to a variant where there are no knapsack constraints but the objective is an arbitrary Lipschitz concave function of the sum of outcome vectors.

Cite this Paper


BibTeX
@InProceedings{pmlr-v49-agrawal16, title = {An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives}, author = {Shipra Agrawal and Nikhil R. Devanur and Lihong Li}, booktitle = {29th Annual Conference on Learning Theory}, pages = {4--18}, year = {2016}, editor = {Vitaly Feldman and Alexander Rakhlin and Ohad Shamir}, volume = {49}, series = {Proceedings of Machine Learning Research}, address = {Columbia University, New York, New York, USA}, month = {23--26 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v49/agrawal16.pdf}, url = {http://proceedings.mlr.press/v49/agrawal16.html}, abstract = {We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be below some pre-fixed budget. The learning agent competes with an arbitrary set of context-dependent policies. This problem was introduced by Badanidiyuru et al., who gave a computationally inefficient algorithm with near-optimal regret bounds for it. We give a \emphcomputationally efficient algorithm for this problem with slightly better regret bounds, by generalizing the approach of Dudik et al. for the non-constrained version of the problem. The computational time of our algorithm scales \emphlogarithmically in the size of the policy space. This answers the main open question of Badanidiyuru et al. We also extend our results to a variant where there are no knapsack constraints but the objective is an arbitrary Lipschitz concave function of the sum of outcome vectors. } }
Endnote
%0 Conference Paper %T An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives %A Shipra Agrawal %A Nikhil R. Devanur %A Lihong Li %B 29th Annual Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2016 %E Vitaly Feldman %E Alexander Rakhlin %E Ohad Shamir %F pmlr-v49-agrawal16 %I PMLR %J Proceedings of Machine Learning Research %P 4--18 %U http://proceedings.mlr.press %V 49 %W PMLR %X We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be below some pre-fixed budget. The learning agent competes with an arbitrary set of context-dependent policies. This problem was introduced by Badanidiyuru et al., who gave a computationally inefficient algorithm with near-optimal regret bounds for it. We give a \emphcomputationally efficient algorithm for this problem with slightly better regret bounds, by generalizing the approach of Dudik et al. for the non-constrained version of the problem. The computational time of our algorithm scales \emphlogarithmically in the size of the policy space. This answers the main open question of Badanidiyuru et al. We also extend our results to a variant where there are no knapsack constraints but the objective is an arbitrary Lipschitz concave function of the sum of outcome vectors.
RIS
TY - CPAPER TI - An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives AU - Shipra Agrawal AU - Nikhil R. Devanur AU - Lihong Li BT - 29th Annual Conference on Learning Theory PY - 2016/06/06 DA - 2016/06/06 ED - Vitaly Feldman ED - Alexander Rakhlin ED - Ohad Shamir ID - pmlr-v49-agrawal16 PB - PMLR SP - 4 DP - PMLR EP - 18 L1 - http://proceedings.mlr.press/v49/agrawal16.pdf UR - http://proceedings.mlr.press/v49/agrawal16.html AB - We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be below some pre-fixed budget. The learning agent competes with an arbitrary set of context-dependent policies. This problem was introduced by Badanidiyuru et al., who gave a computationally inefficient algorithm with near-optimal regret bounds for it. We give a \emphcomputationally efficient algorithm for this problem with slightly better regret bounds, by generalizing the approach of Dudik et al. for the non-constrained version of the problem. The computational time of our algorithm scales \emphlogarithmically in the size of the policy space. This answers the main open question of Badanidiyuru et al. We also extend our results to a variant where there are no knapsack constraints but the objective is an arbitrary Lipschitz concave function of the sum of outcome vectors. ER -
APA
Agrawal, S., Devanur, N.R. & Li, L.. (2016). An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives. 29th Annual Conference on Learning Theory, in PMLR 49:4-18

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