Adversarial imitation learning engine for action risk estimation based on sensor data

A system for classifying components, comprising:

• a sensor data receiver to monitor sensor data from components;
• a transformer-based policy network to process the sensor data to simulate action sequences of the components;
• a generative adversarial network (GAN) including a generator to generate action sequences that mimic low-risk action sequences and a discriminator that distinguishes between generated action sequences and real low-risk action sequences, wherein the GAN generates risk scores for the generated action sequences;
• a candidate identifier that associates the real low-risk action sequences with the components based on the risk scores;
• and
• a communication device that provides a status of the real low-risk action sequences.

A computer-readable medium storing instructions that, when executed by a processor, perform a method for classifying components, comprising:

• monitoring sensors to collect sensor data related to a state of a plurality of components;
• processing, by a computing system, the sensor data to generate an action sequence using a transformer-based policy network for each of the components;
• generating, by the computing system, a risk score for the action sequence using a Generative Adversarial Network (GAN), wherein the GAN includes a generator for generating action sequences and a discriminator to distinguish low-risk action sequences in accordance with a threshold;
• associating, by the computing system, the low-risk action sequences with components in the plurality of components based on the risk score;
• and
• communicating, by the computing system, a status of the low-risk action sequences.