Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
This work addresses the challenge of multi-scenario optimization for large platforms, but it appears incremental as it builds on existing MARL methods.
The paper tackled the problem of optimizing advertising recommendation systems across multiple scenarios by using multi-agent reinforcement learning, resulting in marked improvements in click-through rate, conversion rate, and total sales.
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.