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The online advertising market, with its thousands of auctions run per second,presents a daunting challenge for advertisers who wish to optimize their spendunder a budget constraint. Thus, advertising platforms typically provideautomated agents to their customers, which act on their behalf to bid forimpression opportunities in real time at scale. Because these proxy agents areowned by the platform but use advertiser funds to operate, there is a strongpractical need to balance reliability and explainability of the agent withoptimizing power. We propose a generalizable approach to optimizing biddingpolicies in production environments by learning from real data using offlinereinforcement learning. This approach can be used to optimize anydifferentiable base policy (practically, a heuristic policy based on principleswhich the advertiser can easily understand), and only requires data generatedby the base policy itself. We use a hybrid agent architecture that combinesarbitrary base policies with deep neural networks, where only the optimizedbase policy parameters are eventually deployed, and the neural network part isdiscarded after training. We demonstrate that such an architecture achievesstatistically significant performance gains in both simulated and at-scaleproduction bidding environments. Our approach does not incur additionalinfrastructure, safety, or explainability costs, as it directly optimizesparameters of existing production routines without replacing them with blackbox-style models like neural networks.

Offline Reinforcement Learning for Optimizing Production Bidding Policies