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21.发明申请公开(公告)号：US20200065670A1
公开(公告)日：2020-02-27
申请号：US16518831
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to transfer of training received by a first automated reinforcement-learning-based application manager while controlling a first application is transferred to a second automated reinforcement-learning-based application manager which controls a second application different from the first application. Transferable training provides a basis for automated generation of applications from application components. Transferable training is obtained from composition of applications from application components and composition of reinforcement-learning-based-control-and-learning constructs from reinforcement-learning-based-control-and-learning constructs of application components.
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公开(公告)号：US20200065495A1
公开(公告)日：2020-02-27
申请号：US16502587
申请日：2019-07-03
Applicant: VMware, Inc.
Inventor： Dev Nag , Gregory T. Burk , Yanislav Yankov , Nicholas Mark Grant Stephen , Dongni Wang
Abstract: The current document is directed to a safe-operation-constrained reinforcement-learning-based application manager that can be deployed in various different computational environments, without extensive manual modification and interface development, to manage the computational environments with respect to one or more reward-specified goals. Control actions undertaken by the safe-operation-constrained reinforcement-learning-based application manager are constrained, by stored action filters, to constrain state/action-space exploration by the safe-operation-constrained reinforcement-learning-based application manager to safe actions and thus prevent deleterious impact to the managed computational environment.
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23.发明申请公开(公告)号：US20200065157A1
公开(公告)日：2020-02-27
申请号：US16518763
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to automated reinforcement-learning-based application managers that learn and improve the reward function that steers reinforcement-learning-based systems towards optimal or near-optimal policies. Initially, when the automated reinforcement-learning-based application manager is first installed and launched, the automated reinforcement-learning-based application manager may rely on human-application-manager action inputs and resulting state/action trajectories to accumulate sufficient information to generate an initial reward function. During subsequent operation, when it is determined that the automated reinforcement-learning-based application manager is no longer following a policy consistent with the type of management desired by human application managers, the automated reinforcement-learning-based application manager may use accumulated trajectories to improve the reward function.
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公开(公告)号：US20200065118A1
公开(公告)日：2020-02-27
申请号：US16518617
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to an administrator-monitored reinforcement-learning-based application manager that can be deployed in various different computational environments to manage the computational environments with respect to one or more reward-specified goals. Certain control actions undertaken by the administrator-monitored reinforcement-learning-based application manager are first proposed, to one or more administrators or other users, who can accept or reject the proposed control actions prior to their execution. The reinforcement-learning-based application manager can therefore continue to explore the state/action space, but the exploration can be parametrically constrained as well as by human-administrator oversight and intervention.
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