Abstract: Techniques described herein relate to a method for performing capacity planning services. The method includes obtaining a current CP state from a client; in response to obtaining the current state: selecting an action based on the current CP state; providing the action to the client, wherein the client performs the action; in response to providing the action: obtaining a new CP state and a headcount associated with the action; calculating a reward based on the headcount and a reward formula; storing the current CP state, the action, the new CP state, and the reward as a learning set in storage comprising a plurality of learning sets; and performing a learning update using a portion of the plurality of learning sets to generate an updated actor, an updated critic, an updated target actor, and an updated target critic.

Abstract: Techniques described herein relate to a method for generating address recommendations. The method includes obtaining, by a recommendation system, an address recommendation request associated with an address, wherein the address is associated with a user; in response to obtaining the address recommendation request: generating a context vector associated with the address; generating an address recommendation based on the context vector using a recommendation model; obtaining user feedback associated with the address recommendation; generating a reward based on the user feedback; and updating the recommendation model based on the context vector, the address recommendation and the reward.

Abstract: A system can determine a first persona for a user identity. The system can generate a web page based on the first persona, wherein respective reinforcement learning agents determine respective content of respective content modules of the website, wherein the reinforcement agents operate on an environment that comprises a group of states, a group of actions, and a group of rewards that are based on the group of states, and respective actions of the group of actions, and wherein a reward of the respective reinforcement learning agents that is increased via reinforcement learning is based on a sum of respective rewards of the respective reinforcement learning agents in transitioning the user identity to a second persona with respect to the website. The system can, in response to receiving first action data indicative of user identity interaction with the web page, transition the user identity to the second persona.

Abstract: A system can determine a first persona for a user identity. The system can generate a web page based on the first persona, wherein respective reinforcement learning agents determine respective content of respective content modules of the website, wherein the reinforcement agents operate on an environment that comprises a group of states, a group of actions, and a group of rewards that are based on the group of states, and respective actions of the group of actions, and wherein a reward of the respective reinforcement learning agents that is increased via reinforcement learning is based on a sum of respective rewards of the respective reinforcement learning agents in transitioning the user identity to a second persona with respect to the website. The system can, in response to receiving first action data indicative of user identity interaction with the web page, transition the user identity to the second persona.

Abstract: Machine learning techniques can be implemented to provide personalized websites. In an example, a group of training inputs is determined. A training input of the group of training inputs can comprise a current state that indicates a visit to a website via a user device, an action that indicates a recommendation rendered via the user device during the visit to the website, a user reward that indicates whether user input associated with a user identity via the user device interacted with the recommendation, and a next state that indicates a state of the website for the user device in response to the action being taken from the current state. The training inputs can be used for performing batch offline neural fitted Q iteration training on a neural network to produce a trained neural network. The trained neural network can be stored.