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Online algorithm is an important branch in algorithm design. Designing onlinealgorithms with a bounded competitive ratio (in terms of worst-caseperformance) can be hard and usually relies on problem-specific assumptions.Inspired by adversarial training from Generative Adversarial Net (GAN) and thefact that competitive ratio of an online algorithm is based on worst-caseinput, we adopt deep neural networks to learn an online algorithm for aresource allocation and pricing problem from scratch, with the goal that theperformance gap between offline optimum and the learned online algorithm can beminimized for worst-case input.  Specifically, we leverage two neural networks as algorithm and adversaryrespectively and let them play a zero sum game, with the adversary beingresponsible for generating worst-case input while the algorithm learns the beststrategy based on the input provided by the adversary. To ensure betterconvergence of the algorithm network (to the desired online algorithm), wepropose a novel per-round update method to handle sequential decision making tobreak complex dependency among different rounds so that update can be done forevery possible action, instead of only sampled actions. To the best of ourknowledge, our work is the first using deep neural networks to design an onlinealgorithm from the perspective of worst-case performance guarantee. Empiricalstudies show that our updating methods ensure convergence to Nash equilibriumand the learned algorithm outperforms state-of-the-art online algorithms undervarious settings.

Adversarial Deep Learning for Online Resource Allocation