AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization
This provides a customizable tool for the reinforcement learning community to test algorithms for digital marketing optimization without costly real data, though it is incremental as it builds on existing RL methods for a specific domain.
The authors tackled the problem of optimizing search engine marketing campaigns by introducing AdCraft, a benchmark environment that simulates stochastic and non-stationary bidding and budgeting dynamics, demonstrating challenges like sparsity and non-stationarity that affect agent convergence and performance.
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.