Summary of Hierarchical Multi-agent Meta-reinforcement Learning For Cross-channel Bidding, by Shenghong He and Chao Yu
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding
by Shenghong He, Chao Yu
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hierarchical multi-agent reinforcement learning framework for multi-channel bidding optimization tackles the dynamic budget allocation problem in online advertising ecosystems, where advertisers aim to optimize their ROI and CPC while managing shared budgets across multiple channels. The framework leverages a CPC constrained diffusion model for top-level strategy, addressing channel features and interdependencies, combined with state-action decoupled actor-critic methods to handle offline learning extrapolation errors. Additionally, the framework incorporates context-based meta-channel knowledge learning to improve policy state representation. Experimental results on a large-scale industrial dataset from Meituan’s ad bidding platform demonstrate state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in online advertising. Advertisers want to get the best return on their money while showing ads across different websites and apps. They need to decide how much to spend on each one, but it’s hard because each website is different and advertisers have limited budgets. The authors created a new way for computers to learn from experience and make decisions about where to spend the budget. This approach helps advertisers get the best results while staying within their budget limits. |
Keywords
» Artificial intelligence » Diffusion model » Optimization » Reinforcement learning
