Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising (1802.09756v2)

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.

• The paper demonstrates that a cluster-based multi-agent reinforcement learning framework outperforms traditional bidding strategies using real-world data.
• It employs a Distributed Coordinated Multi-Agent Bidding (DCMAB) model to balance cooperation and competition among advertiser clusters for optimal revenue and ROI.
• The study highlights practical implications for adapting bidding strategies dynamically in competitive online advertising using AI-driven methods.

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

The paper presents an innovative approach to optimizing real-time bidding (RTB) in display advertising using Multi-Agent Reinforcement Learning (MARL). In real-time advertising, advertisers engage in bidding for ad placements targeted at individual user impressions. The primary objective is to optimize measurable outcomes such as revenue and return on investment (ROI). This requires not only estimating the relevance of ads to a user’s interests but also necessitates a strategic response to the bids placed by competing advertisers.

Formulation and Approach

This research formulates the bidding problem within a multi-agent reinforcement learning framework. Given the large number of advertisers, the paper introduces a clustering strategy to manage complexity by assigning a strategic bidding agent to each advertiser cluster. The proposed methodology, named Distributed Coordinated Multi-Agent Bidding (DCMAB), balances cooperation and competition amongst advertisers in the system. This novel approach is aimed at achieving better overall objectives in bidding scenarios compared to purely self-interested bidding agents.

Key Numerical Insights and Claims

The empirical evaluation conducted on industry-scale, real-world data underscores the effectiveness of the DCMAB approach. The paper reveals that the cluster-based strategic bidding agents significantly outperform single-agent and bandit approaches. Furthermore, by facilitating coordinated bidding, DCMAB surpasses the outcomes of strategies reliant on purely self-interested bidding agents. This signifies a critical numerical milestone as DCMAB manages to maximize total traffic revenue and optimize ROI while ensuring effective budget utilization.

Implications and Future Developments

The application of MARL within RTB is a noteworthy addition to the field of online advertising. Practically, this approach enables advertisers to dynamically adapt their strategies in accordance with the evolving competitive landscape in real time. From a theoretical perspective, it advances the understanding of how game-theoretical and cooperative strategies can be applied in multi-agent systems to achieve a social optimum in a competitive environment. This has far-reaching implications for the development of more sophisticated AI systems in marketing and beyond.

The realistic industry setting of Taobao provides a rigorous testing ground that suggests robust future performance when integrated into online systems. Future strides could potentially involve enhancing coordination mechanisms within MARL frameworks or scaling up the approach to encompass additional complexities in user targeting and contextual ad placement.

The work done here extends the capabilities of MARL in large-scale applications and serves as a foundation for further exploration in AI and economic environments. As MARL continues to develop, its potential to provide advanced solutions in advertising and other domains becomes increasingly evident, fostering improved efficiencies and intelligent automation.