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COLT
2006
Springer

Online Learning with Constraints

10 years 3 months ago
Online Learning with Constraints
In this paper, we study a sequential decision making problem. The objective is to maximize the total reward while satisfying constraints, which are defined at every time step. The novelty of the setup is our assumption that the rewards and constraints are controlled by a potentially adverse opponent. To solve the problem, we propose a novel expert algorithm that guarantees a vanishing regret while violating only some bounded number of constraints. The quality of our expert solutions is evaluated on a challenging power management problem. Results of our experiments show that online learning with constraints can be carried out successfully in practice.
Shie Mannor, John N. Tsitsiklis
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where COLT
Authors Shie Mannor, John N. Tsitsiklis
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