公开(公告)号：US20200065128A1

公开(公告)日：2020-02-27

申请号：US16261253

申请日：2019-01-29

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Gregory T. Burk ,  Janislav Jankov ,  Nick Stephen ,  Dongni Wang

IPC: G06F9/455 ,  G06F9/50 ,  G06F9/48 ,  G06N20/00 ,  G06N3/08

Abstract: The current document is directed to a modular reinforcement-learning-based application manager that can be deployed in various different computational environments without extensive modification and interface development. The currently disclosed modular reinforcement-learning-based application manager interfaces to observation and action adapters and metadata that provide a uniform and, in certain implementations, self-describing external interface to the various different computational environments which the modular reinforcement-learning-based application manager may be operated to control. In addition, certain implementations of the currently disclosed modular reinforcement-learning-based application manager interface to a user-specifiable reward-generation interface to allow the rewards that provide feedback from the computational environment to the modular reinforcement-learning-based application manager to be tailored to meet a variety of different user expectations and desired control policies.

公开(公告)号：US10802864B2

公开(公告)日：2020-10-13

申请号：US16261253

申请日：2019-01-29

Applicant: VMware, Inc.

Inventor： Dev Nag ,  Gregory T. Burk ,  Janislav Jankov ,  Nick Stephen ,  Dongni Wang

IPC: G06F9/455 ,  G06F9/50 ,  G06N20/00 ,  G06N3/08 ,  G06F9/48

Abstract: The current document is directed to a modular reinforcement-learning-based application manager that can be deployed in various different computational environments without extensive modification and interface development. The currently disclosed modular reinforcement-learning-based application manager interfaces to observation and action adapters and metadata that provide a uniform and, in certain implementations, self-describing external interface to the various different computational environments which the modular reinforcement-learning-based application manager may be operated to control. In addition, certain implementations of the currently disclosed modular reinforcement-learning-based application manager interface to a user-specifiable reward-generation interface to allow the rewards that provide feedback from the computational environment to the modular reinforcement-learning-based application manager to be tailored to meet a variety of different user expectations and desired control policies.