Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
<|reference_start|>Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System: This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.<|reference_end|>
@article{zhao2024multi-scenario,
    title={Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning
  to Optimize the Advertising Recommendation System},
    author={Yang Zhao, Chang Zhou, Jin Cao, Yi Zhao, Shaobo Liu, Chiyu Cheng,
  Xingchen Li},
    journal={arXiv preprint arXiv:2407.02759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02759},
    primaryClass={cs.LG cs.AI}
}