Abstract: An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to classify telemetry data into a set of categories. The processor implements a communication synchronization scheme to receive a first set of telemetry data associated with a first user and a second set of telemetry data associated with a second user. The processor applies the machine learning algorithm to each of the first and second sets of telemetry data, to classify the data. The processor transmits, to a server, training data that includes at least the classified data or a set of parameters derived from the classified data. The server uses the training data to refine a reinforcement learning algorithm that is configured to generate a recommendation of computational resources to provision to a new user.

Abstract: An apparatus includes a processor and a memory that stores a deep Q reinforcement learning (DQN) algorithm configured to generate an action, based on a state. Each action includes a recommendation associated with a computational resource. Each state identifies at least a role within an enterprise. The processor receives information associated with a first user, including an identification of a first role assigned to the user and computational resource information associated with the user. The processor applies the DQN algorithm to a first state, which includes an identification of the first role, to generate a first action, which includes a recommendation associated with a first computational resource. In response to applying the DQN algorithm, the processor generates a reward value based on the alignment between the first recommendation and the computational resource information associated with the first user. The processor uses the reward value to update the DQN algorithm.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to generate, based on a given role of a plurality of roles within an enterprise, a policy for a new user assigned to the given role. The policy includes recommendations of virtual desktop resources of an enterprise system to provide to the new user. The machine learning algorithm was trained using information identifying virtual desktop resources used by a set of existing users of the system, each of whom is assigned to a role of the plurality of roles within the enterprise. The processor receives a request to provide a new user with access to the enterprise system. In response, the processor implements the machine learning algorithm to generate the policy for the new user, and subsequently provisions the new user with the one or more virtual desktop resources recommended by the policy.