Proxy interpreter to upgrade automated legacy systems

A method of training the proxy interpreter system to build an Artificial intelligence module through deep learning modules, used to select actions to be performed by interacting with the legacy system and by receiving observations of the operating sequence and stales of the system, wherein the method comprises:

• obtaining a set of activities triggered from the legacy system interactive environment, with each activity comprising a process characterizing a set of events and a related command or set of commands in response to the activity;
• building domain know ledge through reinforcement learning of the multiple operating states of the legacy system, using a Double Deep Q Network implementation to create a set of recipe file with various parameters for a specific device type;
• processing the observations and their related actions during the legacy system setup and operation and implementing a method of of rewards and penalty for positive and negative behaviour to arrive at an optimum behavioral model to operate the legacy system effectively;
• creating a confidence vector for every single action, for use as a filter to prevent a response by the action classifier for selected irrelevant operating states of the legacy system;
• Constantly reviewing and updating the Advantage stream of the Reinforcement Learning module by implementing the concept of “Duelling Double Deep Q network”