  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.
