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Abstract

The technology described herein trains a reinforcement-learning model in a simulated environment. A simulated environment contrasts with a live environment. A live environment is a computing environment with which the reinforcement-learning model will interact once it is deployed. In order to be effective, the simulated environment may provide inputs to the reinforcement-learning model in the same format as the reinforcement-learning model receives from the live environment. In aspects, the training in the simulated environment may act as pre-training for training in the live environment. Once pre-trained, the reinforcement-learning model may be deployed in a live environment and continue to learn how to perform the same task in different ways, learn how to perform additional tasks, and/or improve performance of a task learned in pre-training. In aspects, the reinforcement-learning model may be used to discover unhealthy conditions in software by performing the tasks it has learned.

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