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

TL;DR

This paper proposes a multi-agent reinforcement learning approach, MARDPG, to optimize multiple advertising-related scenarios simultaneously, significantly improving key performance metrics on large platforms.

Contribution

Introduction of the MARDPG algorithm for multi-scenario optimization in advertising, enabling cooperative decision-making across different platform functions.

Findings

Improved click-through rate (CTR) and conversion rate

Enhanced total sales in practical applications

Effective multi-scenario cooperation demonstrated

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.