Offline Reinforcement Learning for Optimizing Production Bidding Policies

• URL: http://arxiv.org/abs/2310.09426v1
• Date: Fri, 13 Oct 2023 22:14:51 GMT
• Title: Offline Reinforcement Learning for Optimizing Production Bidding Policies
• Authors: Dmytro Korenkevych, Frank Cheng, Artsiom Balakir, Alex Nikulkov, Lingnan Gao, Zhihao Cen, Zuobing Xu, Zheqing Zhu
• Abstract summary: We propose a generalizable approach to optimizing bidding policies in production environments. We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks. We demonstrate that such an architecture achieves statistically significant performance gains in both simulated and at-scale production bidding environments.
• Score: 1.8689461238197953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constraint. Thus, advertising platforms typically provide automated agents to their customers, which act on their behalf to bid for impression opportunities in real time at scale. Because these proxy agents are owned by the platform but use advertiser funds to operate, there is a strong practical need to balance reliability and explainability of the agent with optimizing power. We propose a generalizable approach to optimizing bidding policies in production environments by learning from real data using offline reinforcement learning. This approach can be used to optimize any differentiable base policy (practically, a heuristic policy based on principles which the advertiser can easily understand), and only requires data generated by the base policy itself. We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training. We demonstrate that such an architecture achieves statistically significant performance gains in both simulated and at-scale production bidding environments. Our approach does not incur additional infrastructure, safety, or explainability costs, as it directly optimizes parameters of existing production routines without replacing them with black box-style models like neural networks.

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