Abstract: Methods and systems are disclosed for determining a plan to optimize key performance indicators (KPIs) of an industrial process. Such a plan is determined based on generating a query for information associated with the KPIs and based on receiving user-provided object information corresponding to the KPIs. The method includes receiving, at a user interface, one or more KPIs associated with an industrial process. The method includes generating, based on the one or more KPIs, at least one query for information associated with the KPI. The method includes receiving, at the user interface, a response to the at least one query. The method includes determining, by an artificial intelligence agent, a plan for optimizing the KPI.

Abstract: Methods and systems are disclosed for determining a plan to optimize key performance indicators (KPIs) of an industrial process. Such a plan is determined based on generating a query for information associated with the KPIs and based on receiving user-provided object information corresponding to the KPIs. The method includes receiving, at a user interface, one or more KPIs associated with an industrial process. The method includes generating, based on the one or more KPIs, at least one query for information associated with the KPI. The method includes receiving, at the user interface, a response to the at least one query. The method includes determining, by an artificial intelligence agent, a plan for optimizing the KPI.

Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: Methods and systems are disclosed for determining a plan to optimize key performance indicators (KPIs) of an industrial process. Such a plan is determined based on generating a query for information associated with the KPIs and based on receiving user-provided object information corresponding to the KPIs. The method includes receiving, at a user interface, one or more KPIs associated with an industrial process. The method includes generating, based on the one or more KPIs, at least one query for information associated with the KPI. The method includes receiving, at the user interface, a response to the at least one query. The method includes determining, by an artificial intelligence agent, a plan for optimizing the KPI.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.

Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.

Abstract: Methods, systems, apparatus and computer program products for implementing machine learning within control systems are disclosed. An industrial facility setting slate can be received from a machine learning system and a determination can be made as to whether to adopt the settings in the industrial facility setting slate. The machine learning model can be a neural network, e.g., a deep neural network, that has been trained, e.g., using reinforcement learning to predict a data setting slate that is predicted to optimize an efficiency of a data center.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.