Online displays are majorly served through real-time bidding where each impression of the display advertisement is auctioned in real time simultaneously when generated from a user visit. Placing a bid automatically, and in real time, is highly critical for advertisers to maximize their profits. Thus, a learning algorithm needs to be devised that can devise an optimal learning strategy in real time based on historical data, so that dynamic allocation of the budget takes place across different impressions according to immediate and future returns. Here, we will discuss formulating a bid-decision process in terms of a reinforcement learning framework published in Real-Time Bidding by Reinforcement Learning in Display Advertising by Cai et. al. 2017.
In this research by Cai et. al., the machine bidding in the...
