The article presents a new dynamic bidding strategy for online advertising that uses reinforcement learning to adapt to changes in the ad delivery environment. By dividing the ad delivery period into time slots, the bidding agent can adjust the bidding price for each impression based on its estimated value and the current bidding factor. The study shows that this approach outperforms other strategies in maximizing revenues like clicks and conversions in a highly dynamic real-time bidding environment.