Patent ID: 11922323
Assignee: SALESFORCE, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A non-transitory machine-readable medium comprising executable code which when executed by one or more processors associated with a computing device are adapted to cause the one or more processors to perform a method comprising:
receiving a distribution including a plurality of related tasks; and
training parameters for a reinforcement learning neural network model based on gradient estimation associated with the parameters using samples associated with the plurality of related tasks,
wherein control variates are incorporated into the gradient estimation using a combination of a first gradient estimator and a second gradient estimator,
wherein the first gradient estimator uses the control variates and the second gradient estimator does not use the control variates,
wherein the control variates are represented by a baseline function depending on the tasks and corresponding states of the tasks,
wherein the gradient estimation is generated using Hessian estimation, and
wherein a product of two factors for the Hessian estimation based on the first gradient estimator and the second gradient estimator is reduced by subtracting the baseline function from one of the two factors.