Improving real-time bidding using a constrained markov decision process

Abstract

Online advertising is increasingly switching to real-time bidding on advertisement inventory, in which the ad slots are sold through real-time auctions upon users visiting websites or using mobile apps. To compete with unknown bidders in such a highly stochastic environment, each bidder is required to estimate the value of each impression and to set a competitive bid price. Previous bidding algorithms have done so without considering the constraint of budget limits, which we address in this paper. We model the bidding process as a Constrained Markov Decision Process based reinforcement learning framework. Our model uses the predicted click-through-rate as the state, bid price as the action, and ad clicks as the reward. We propose a bidding function, which outperforms the state-of-the-art bidding functions in terms of the number of clicks when the budget limit is low. We further simulate different bidding functions competing in the same environment and report the performances of the bidding strategies when required to adapt to a dynamic environment.