AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization

• URL: http://arxiv.org/abs/2306.11971v3
• Date: Tue, 14 Nov 2023 21:01:26 GMT
• Title: AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization
• Authors: Maziar Gomrokchi, Owen Levin, Jeffrey Roach, Jonah White
• Abstract summary: We introduce AdCraft, a novel benchmark environment for the Reinforcement Learning (RL) community. The environment simulates bidding and budgeting dynamics within Search Engine Marketing (SEM), a digital marketing technique. We demonstrate the challenges imposed on agent convergence and performance by sparsity and non-stationarity.
• Score: 0.6554326244334868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce AdCraft, a novel benchmark environment for the Reinforcement Learning (RL) community distinguished by its stochastic and non-stationary properties. The environment simulates bidding and budgeting dynamics within Search Engine Marketing (SEM), a digital marketing technique utilizing paid advertising to enhance the visibility of websites on search engine results pages (SERPs). The performance of SEM advertisement campaigns depends on several factors, including keyword selection, ad design, bid management, budget adjustments, and performance monitoring. Deep RL recently emerged as a potential strategy to optimize campaign profitability within the complex and dynamic landscape of SEM, but it requires substantial data, which may be costly or infeasible to acquire in practice. Our customizable environment enables practitioners to assess and enhance the robustness of RL algorithms pertinent to SEM bid and budget management without such costs. Through a series of experiments within the environment, we demonstrate the challenges imposed on agent convergence and performance by sparsity and non-stationarity. We hope these challenges further encourage discourse and development around effective strategies for managing real-world uncertainties.

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