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arXiv · 2407.02759

Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System

Yang Zhao·Chang Zhou·Jin Cao·Yi Zhao·Shaobo Liu·Chiyu Cheng·Xingchen Li
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Abstract

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.

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Yang Zhao, Chang Zhou, Jin Cao, Yi Zhao, Shaobo Liu, Chiyu Cheng, Xingchen Li. 2024-07-03. Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System. https://arxiv.org/abs/2407.02759

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