Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

TL;DR

This paper introduces a multi-agent reinforcement learning framework for real-time bidding in display advertising, employing clustering and coordination strategies to improve bidding effectiveness and outperform existing methods.

Contribution

It proposes a novel distributed multi-agent reinforcement learning approach with clustering and coordination for real-time bidding in display advertising.

Findings

Cluster-based bidding outperforms single-agent and bandit methods.

Coordinated bidding achieves better overall objectives.

Empirical results on industry-scale data validate effectiveness.

Abstract

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.