Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to access data about an environment of an agent, identify an actor in the environment, and access candidate models, in which each of the candidate models may predict a certain action of the identified actor. The instructions may also cause the processor to apply a selected candidate model of the accessed candidate models on the accessed data to determine a predicted action of the identified actor and may implement a recursive reinforcement learning model using the predicted action of the identified actor to determine an action that the agent is to perform. The instructions may further cause the processor to cause the agent to perform the determined action.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to access data about an environment of an agent, identify an actor in the environment, and access candidate models, in which each of the candidate models may predict a certain action of the identified actor. The instructions may also cause the processor to apply a selected candidate model of the accessed candidate models on the accessed data to determine a predicted action of the identified actor and may implement a recursive reinforcement learning model using the predicted action of the identified actor to determine an action that the agent is to perform. The instructions may further cause the processor to cause the agent to perform the determined action.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to access data about an environment of an agent, identify an actor in the environment, and access candidate models, in which each of the candidate models may predict a certain action of the identified actor. The instructions may also cause the processor to apply a selected candidate model of the accessed candidate models on the accessed data to determine a predicted action of the identified actor and may implement a recursive reinforcement learning model using the predicted action of the identified actor to determine an action that the agent is to perform. The instructions may further cause the processor to cause the agent to perform the determined action.

Abstract: Systems are provided for using machine learning to identify service accounts and/or for distinguishing service accounts from user accounts based on the user names of the accounts. Machine learning tools can be trained on user name label data for service accounts and user accounts. The trained machine learning tool can then be applied to user names of accounts to determine whether the user names correspond to service accounts or not and, in some instances, without referencing tables or other structures that explicitly identify and distinguish the service/user accounts and/or conventions for identifying service accounts. Then, the systems can respond appropriately, based on the determination. The machine learning tool can also be shared with other systems to make the same determinations for their accounts without having to share confidential or proprietary account information.