Balancing Cost Efficiency and Campaign Success: Strategic Bidding in First-Price Auctions

Abstract

Real-time bidding (RTB) is an essential mechanism of virtual advertising, allowing advertisers to compete for ad impressions in real-time auctions that finalize in an instant. In first-price auctions, where the highest bidder is declared the winner and their bill is exactly their submitted bid, bidders must balance between securing impressions and controlling costs. This opens up new problems such as bid shading, where advertisers bid below their true valuation to avoid overpaying, and ad allocation, which involves selecting the optimal ad for each impression to maximize campaign effectiveness. To tackle these problem, we put forward a framework based on Deep Q-Learning (DQL). The framework jointly optimizes bidding strategies and ad selection in first-price RTB environments. By modelling the auction as an Episodic Markov Decision Process (EMDP), our agent learns to make decisions that consider both short-term auction outcomes and long-term budget management. The reward function is designed to reflect key trade-offs between bid competitiveness, budget utilization, and campaign objectives, allowing the agent to dynamically adjust bids and allocate ads in real time. Our approach leverages the Auction-Gym simulation environment for evaluation, where we demonstrate its superiority over traditional bidding methods through extensive experiments. The results reveal significant improvements in cost efficiency, win rates, and campaign performance, showcasing the potential of DQL in high-stakes decision-making like the first-price RTB auctions. Furthermore, our model adapts to changing market conditions, making it a robust solution for modern advertisers looking to optimize their bidding strategies in a competitive and budget-constrained environment.