AI Research
HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
Online advertising bidding systems typically deploy multiple offline-trained expert models (e.g., PID controllers, model predictive control, offline RL policies) but face two critical limitations: lack of online adaptability to non-stationary auction markets, and reliance on cost...
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ArXiv CS.AI
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ArXiv CS.AI
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This research introduces a hierarchical reinforcement learning approach for online advertising that leverages LLMs for strategy and expert agents for execution, enabling real-time adaptation to market changes and reducing manual tuning. This holistic framework addresses key challenges in automated bidding, leading to improved performance in real-world deployments.
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