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.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that that are trained using adversarial training. During adversarial training, potentially disadvantageous next actions are selected for issuance by an automated reinforcement-learning-based application manager at a lower frequency than selection of next actions, according to a policy that is learned to provide optimal or near-optimal control over a computing environment that includes one or more applications controlled by the automated reinforcement-learning-based application manager.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that use local agents. Local agents provide finer-granularity monitoring of an application or application subcomponents and provide continued application management in the event of interruption of network traffic between an automated reinforcement-learning-based application manager and the application or application subcomponents managed by the automated reinforcement-learning-based application manager.

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.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that obtain increased computational efficiency by reusing learned models and by using human-management experience to truncate state and observation vectors. Learned models of managed environments that receive component-associated inputs can be partially or completely reused for similar environments. Human managers and administrators generally use only a subset of the available metrics in managing an application, and that subset can be used as an initial subset of metrics for learning an optimal or near-optimal control policy by an automated reinforcement-learning-based application manager.

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.

Abstract: The current document is directed to a resource-identifier-correlation service and/or application that maintains correlation information about the different resource identifiers used by different management applications and/or services within a cloud-computing facility or distributed cloud-computing facility. In one implementation, the resource-identifier-correlation service and/or application continuously monitors streams of inventory/configuration data for different management applications and/or services in order to construct and maintain a database of computational resources and the resource identifiers associated with the computational resources. The resource-identifier-correlation service and/or application includes one or both of a service interface and an application programming interface (“API”) that provide many different functionalities related to identifying and monitoring correlations between resource identifiers used by the different management applications and/or services.

Abstract: In an illustrative embodiment, an adjustable cradle assembly for adjusting a head position of a patient relative to a patient platform includes a base portion with a pair of vertical support members and a cradle portion. The vertical support members may each include at least one position aperture for setting a vertical height of the cradle portion relative to the base portion. The cradle portion may include a channel for receiving a head fixation ring. The cradle portion may include at least one set of adjustment connection points for aligning with position apertures of each vertical support member. The cradle may be pivotably connected to the vertical support members such that a pitch angle of a head position of a patient secured in the head fixation apparatus may be adjusted. A set of adjustment mechanisms may releasably secure the cradle to the base at a selected lateral and/or pitch position.

Abstract: In an embodiment, a method for effecting thermal therapy using an in vivo probe includes positioning the probe in a volume in a patient, identifying an irregularly shaped three-dimensional region of interest and automatically applying thermal therapy to the region using the probe. Applying thermal therapy may include identifying a first emission level at a first rotational angle based in part on a depth of a radial portion of the region in the direction of probe emission, activating emission of the probe, causing rotation of the probe to a next rotational angle, identifying a next emission level at the next rotational angle based in part on a depth of a radial portion of the region in the direction of probe emission, activating emission to deliver therapeutic energy, and repeating rotation and emission until therapeutic energy has been delivered to the volume.

Abstract: An example method comprising receiving volunteer information, receiving service opportunity information, generate a query based on at least some of the volunteer information to identify one or more service opportunities, searching, using the query based on at least some of the volunteer information, to identify the one or more service opportunities from the service opportunity information, providing a first list of service opportunities based on the one or more service opportunities that were identified based on the search, receiving a selection of a service opportunity from the list of service opportunities, scheduling a volunteer associated with the volunteer system for service associated with the selected service opportunity, notifying at least one of the unrelated service opportunity systems associated with the selected service opportunity, receiving an indication of completion of the selected service opportunity and an indication of the volunteer's participation in the selected service opportunity, and u

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.

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.

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.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that that are trained using adversarial training. During adversarial training, potentially disadvantageous next actions are selected for issuance by an automated reinforcement-learning-based application manager at a lower frequency than selection of next actions, according to a policy that is learned to provide optimal or near-optimal control over a computing environment that includes one or more applications controlled by the automated reinforcement-learning-based application manager.

Abstract: The current document is directed to an automated reinforcement-learning-based application manager that uses action tags and metric tags. In various implementations, actions and metrics are associated with tags. Different types of tags can contain different types of information that can be used to greatly improve the computational efficiency by which the reinforcement-learning-based application manager explores the action-state space in order to determine and maintain an optimal or near-optimal management policy by providing a vehicle for domain knowledge to influence control-policy decision making.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that obtain increased computational efficiency by reusing learned models and by using human-management experience to truncate state and observation vectors. Learned models of managed environments that receive component-associated inputs can be partially or completely reused for similar environments. Human managers and administrators generally use only a subset of the available metrics in managing an application, and that subset can be used as an initial subset of metrics for learning an optimal or near-optimal control policy by an automated reinforcement-learning-based application manager.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that use local agents. Local agents provide finer-granularity monitoring of an application or application subcomponents and provide continued application management in the event of interruption of network traffic between an automated reinforcement-learning-based application manager and the application or application subcomponents managed by the automated reinforcement-learning-based application manager.

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.

Abstract: The current document is directed to methods and systems for simulation-based training of automated reinforcement-learning-based application managers. Simulators are generated from data collected from controlled computing environments controlled and may employ any of a variety of different machine-learning models to learn state-transition and reward models. The current disclosed methods and systems provide facilities for visualizing aspects of the models learned by a simulator and for initializing simulator models using domain information. In addition, the currently disclosed simulators employ weighted differences computed from simulator-generated and training-data state transitions for feedback to the machine-learning models to address various biases and deficiencies of commonly employed difference metrics in the context of training automated reinforcement-learning-based application managers.

Abstract: A variable length interstitial probe apparatus includes: a probe for effecting thermal therapy and/or cryotherapy to a tissue; a flexible umbilical sheath permanently affixed to the probe, including at least one interface for supplying energy, cooling fluid, cooling gas, heating fluid, and/or heating gas to the probe; and an adjustable depth stop configured to slide along a length of a shaft region of the probe, and lock to the shaft region at a selected position. The adjustable depth stop is configured to engage a probe driver and/or a skull mount apparatus to stabilize positioning of the probe and to control a depth of entry of the probe into a patient. The probe may be configured to effect temperature modulation therapy, where processing circuitry activates a modulation pattern of thermal therapy emission and cryogenic therapy emission for applying a thermal dose to the tissue.