Abstract: A user interface (UI), for analyzing model training runs, tracking and visualizing various aspects of machine learning experiments, can be used when training an artificial intelligent agent in, for example, a racing game environment. The UI can be web-based and can allow researchers to easily see the status of their experiments. The UI can include an experiment synchronized event viewer that can synchronizes visualizations, videos, and timeline/metrics graphs in the experiment. This viewer allows researchers to see how experiments unfold in great detail. The UI can further include experiment event annotations that can generate event annotations. These annotations can be displayed via the synchronized event viewer. The UI can be used to consider consolidated results across experiments and can further consider videos. For example, the UI can provide a reusable dashboard that can capture and compare metrics across multiple experiments.

Abstract: An artificial intelligent agent can act as a player in a video game, such as a racing video game. The game can be completely external to the agent and can run in real time. In this way, the training system is much more like a real world system. The consoles on which the game runs for training the agent are provided in a cloud computing environment. The agents and the trainers can run on other computing devices in the cloud, where the system can choose the trainers and agent compute based on proximity to console, for example. Users can choose the game they want to run and submit code which can be built and deployed to the cloud system. A resource management service can monitor game console resources between human users and research usage and identify experiments for suspension to ensure enough game consoles for human users.

Abstract: An artificial intelligent agent can act as a player in a video game, such as a racing video game. The game can be completely external to the agent and can run in real time. In this way, the training system is much more like a real world system. The consoles on which the game runs for training the agent are provided in a cloud computing environment. The agents and the trainers can run on other computing devices in the cloud, where the system can choose the trainers and agent compute based on proximity to console, for example. Users can choose the game they want to run and submit code which can be built and deployed to the cloud system. A resource management service can monitor game console resources between human users and research usage and identify experiments for suspension to ensure enough game consoles for human users.

Abstract: An artificial intelligent agent can act as a player in a video game, such as a racing video game. The agent can race against, and often beat, the best players in the world. The game can be completely external to the agent and can run in real time. In this way, the training system is much more like a real world system. The consoles on which the game runs for training the agent are provided in a cloud computing environment. The agents and the trainers can run on other computing devices in the cloud, where the system can choose the trainers and agent compute based on proximity to console, for example. Users can choose the game they want to run and submit code which can be built and deployed to the cloud system. Metrics and logs and artifacts from the game can be sent to cloud storage.

Abstract: Various methods, apparatuses/systems, and media for PaaS cloud ready random access report generation are disclosed. A processor receives an initial intermediate file having intermediate contents to be utilized for PaaS cloud ready random access report generation; determines whether the intermediate contents exceed a predetermined memory threshold value; implements a first mode of report generation algorithm to create a final intermediate file when it is determined that a memory requirement for the intermediate contents is equal to or below the predetermined memory threshold value or implement a second mode of report generation algorithm to create the final intermediate file when it determined that the memory requirement for the intermediate contents exceed the predetermined memory threshold value; transmits the final intermediate file to a rendering engine; and generates a report based on the final intermediate file.

Abstract: Various methods, apparatuses/systems, and media for PaaS cloud ready random access report generation are disclosed. A processor receives an initial intermediate file having intermediate contents to be utilized for PaaS cloud ready random access report generation; determines whether the intermediate contents exceed a predetermined memory threshold value; implements a first mode of report generation algorithm to create a final intermediate file when it is determined that a memory requirement for the intermediate contents is equal to or below the predetermined memory threshold value or implement a second mode of report generation algorithm to create the final intermediate file when it determined that the memory requirement for the intermediate contents exceed the predetermined memory threshold value; transmits the final intermediate file to a rendering engine; and generates a report based on the final intermediate file.

Abstract: Techniques for custom time series analysis with large-scale datasets are described. A time series data analysis service provides an interactive development environment that is configured to accept user input to configure stages of a time series analysis data pipeline. The stages include one or more of a collection stage to place events from a dataset into groupings of ones of the events, a summary stage to generate a set of summary statistics based on the groupings of events, a fill and filter stage to add or remove summary statistics to or from the set of summary statistics, and/or an analytics stage to apply analytical functions based at least in part on the set of summary statistics. The stages can be executed at least partially in a distributed manner by a cluster of computing instances executing an analytics engine.

Abstract: Reinforcement learning methods can use actor-critic networks where (1) additional laboratory-only state information is used to train a policy that much act without this additional laboratory-only information in a production setting; and (2) complex resource-demanding policies are distilled into a less-demanding policy that can be more easily run at production with limited computational resources. The production actor network can be optimized using a frozen version of a large critic network, previously trained with a large actor network. Aspects of these methods can leverage actor-critic methods in which the critic network models the action value function, as opposed to the state value function.

Abstract: A real-time temporal convolution network (RT-TCN) algorithm reuses the output of prior convolution operations in all layers of the network to minimize the computational requirements and memory footprint of a TCN during real-time evaluation. Further, a TCN trained via the fixed-window view, where the TCN is trained using fixed time splices of the input time series, can be executed in real-time continually using RT-TCN.

Abstract: In an embodiment, an electronic device may include a processor that may iteratively simulate candidate control trajectories using upper confidence bound for trees (UCT) to control an environmental control system (e.g., an HVAC system). Each candidate control trajectory may be simulated by selecting a control action at each of a plurality of time steps over a period of time that has the highest upper bound on possible performance using values from previous simulations and predicting a temperature for a next time step of the plurality of time steps that results from applying the selected control action using a thermal model. The processor may determine a value of each candidate control trajectory using a cost function, update the value of each control action selected in each candidate control trajectory, and select a candidate control trajectory with the highest value using UCT to apply to control the environmental control system.

Abstract: In an embodiment, an electronic device may include a power source configured to provide operational power to the electronic device and a processor coupled to the power source. The processor may be configured to generate temperature predictions using a model of a structure and possible control scenarios, determine a value of the temperature predictions and the respective possible control scenarios using a cost function, the cost function comprising weighted factors related to an error between a setpoint temperature and the temperature predictions, a length of runtime for an environmental control system (e.g., an HVAC system), and a length of environmental control system cycles. The processor may also be configured to select the control scenario with the highest value to apply to control the environmental control system. The control scenarios may be generated using upper confidence bound for trees (UCT).

Abstract: In an embodiment, an electronic device may include a processor that may iteratively simulate candidate control trajectories using upper confidence bound for trees (UCT) to control an environmental control system (e.g., an HVAC system). Each candidate control trajectory may be simulated by selecting a control action at each of a plurality of time steps over a period of time that has the highest upper bound on possible performance using values from previous simulations and predicting a temperature for a next time step of the plurality of time steps that results from applying the selected control action using a thermal model. The processor may determine a value of each candidate control trajectory using a cost function, update the value of each control action selected in each candidate control trajectory, and select a candidate control trajectory with the highest value using UCT to apply to control the environmental control system.