Abstract: The techniques disclosed herein enable systems to enhance the resilience of autonomous control systems through a fault-tolerant machine learning architecture. To achieve this, a fault-tolerant machine learning agent is constructed with a selector agent, a nominal agent, and a redundancy agent which is a multidimensional lookup table. The fault-tolerant machine learning agent extracts state data from an environment containing a control system and various components. The nominal agent and the redundancy agent generate actions for application to the control system based on the state data which are provided to the selector agent. Based on an analysis of the state data, the selector agent can detect a failure condition. In the event of a failure condition, the selector agent deploys the action generated by the redundancy agent lookup table to resolve the failure condition and restore normal operations.

Abstract: The techniques disclosed herein enable systems to measure the long-term reliability of machine learning agents prior to deployment at a control system. This is achieved through analysis of control system component specifications to determine a useful lifespan of the components such as projected failure rate, hours continuous operation, and so forth. The system can derive parameters for the machine learning agent to interact with the components such as action frequency and action range. From the component lifespan, action frequency, and action range, an accelerated test procedure is constructed to evaluate the reliability of the machine learning agent. From executing the accelerated test procedure, a reliability score can be calculated for the machine learning agent.

Abstract: The techniques disclosed herein enable systems to enhance the resilience of autonomous control systems through a fault-tolerant machine learning architecture. To achieve this, a fault-tolerant machine learning agent is constructed with a selector agent, a nominal agent, and a redundancy agent which is a multidimensional lookup table. The fault-tolerant machine learning agent extracts state data from an environment containing a control system and various components. The nominal agent and the redundancy agent generate actions for application to the control system based on the state data which are provided to the selector agent. Based on an analysis of the state data, the selector agent can detect a failure condition. In the event of a failure condition, the selector agent deploys the action generated by the redundancy agent lookup table to resolve the failure condition and restore normal operations.

Abstract: The techniques disclosed herein enable systems to measure the long-term reliability of machine learning agents prior to deployment at a control system. This is achieved through analysis of control system component specifications to determine a useful lifespan of the components such as projected failure rate, hours continuous operation, and so forth. The system can derive parameters for the machine learning agent to interact with the components such as action frequency and action range. From the component lifespan, action frequency, and action range, an accelerated test procedure is constructed to evaluate the reliability of the machine learning agent. From executing the accelerated test procedure, a reliability score can be calculated for the machine learning agent.

Abstract: The techniques disclosed herein enable systems to enhance autonomous process control platforms using a quality aware machine learning agent. To achieve this, a machine learning agent is integrated into a process control system. The machine learning agent extracts a set of states from an environment containing the process and defines a set of corresponding quality states which are then extracted from the environment as well. Based on the set of states and quality states, the machine learning agent determines a set of actions that modify operating parameters of the process. Applying the actions results in an updated set of states and quality states which can be analyzed to compute an optimality score, quantifying the effectiveness of the actions. Based on the updated states and quality states, the machine learning agent determines a modified set of actions to apply to the environment and increase the optimality score.