Abstract: The present disclosure generally relates to a method that involves in training a machine-learning model using historical data to generate predictive outputs for future routing performance. The method determines optimal routing values based on the model's predictions and uses a reinforcement learning algorithm to select these values from a set of candidates. A positive transmission feedback is generated when a data packet is successfully transmitted, which updates the selection model, thereby refining future routing decisions. The determined routing values are transmitted to a network management system to optimize data packet routing through the network.

Abstract: The present disclosure generally relates to a ticketing platform configured to use machine-learning models to predict an optimal bid value for bidding on objects of content data. More particularly, the present disclosure relates to systems and methods for training machine-learning techniques to generate outputs predictive of the optimal bids for placing objects of content data on, for example, search result pages by using the machine-learning model to detect patterns in various signals, to implement a more efficient resource usage across campaigns.