Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
This work addresses budget optimization for advertisers in online display advertising, representing an incremental improvement over prior model-based methods by enhancing scalability for industrial applications.
The paper tackles the problem of budget-constrained bidding in real-time display advertising by formulating it as a Markov Decision Process and proposing a model-free reinforcement learning framework with an innovative reward function design to address misleading immediate rewards under constraints. Experimental evaluations on large-scale real datasets demonstrate the framework's effectiveness, though no specific numerical results are provided in the abstract.
Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.