Microsoft Technology Licensing, LLC

Victor Shnayder

Ross Ian Story

Eric P Traut

Marcos M Campos

Matthew James Brown

Ruofan Kong

Aditya Gudimella

Marcos De Moura Campos

Matineh Shaker

Xuan Zhao

A method of training a reinforcement machine learning computer system. The method comprises providing a machine-learning computer programming language including a pre-defined plurality of reinforcement machine learning criterion statements, and receiving a training specification authored in the machine-learning computer programming language. The training specification defines a plurality of training sub-goals with a corresponding plurality of the reinforcement machine learning criterion statements supported by the machine-learning computer programming language. The method further comprises computer translating the plurality of training sub-goals from the training specification into a shaped reward function configured to score a reinforcement machine learning model configuration with regard to the plurality of training sub-goals. The method further comprises running a training experiment with the reinforcement machine learning model configuration, scoring the reinforcement machine learning model in the training experiment with the shaped reward function, and adjusting the reinforcement machine learning model configuration based on the shaped reward function.

Training reinforcement machine learning systems

Methods and apparatuses that apply a hierarchical-decomposition reinforcement learning technique to train one or more AI objects as concept nodes composed in a hierarchical graph incorporated into an AI model. The individual sub-tasks of a decomposed task may correspond to its own concept node in the hierarchical graph and are initially trained on how to complete their individual sub-task and then trained on how the all of the individual sub-tasks need to interact with each other in the complex task in order to deliver an end solution to the complex task. Next, during the training, using reward functions focused for solving each individual sub-task and then a separate one or more reward functions focused for solving the end solution of the complex task. In addition, where reasonably possible, conducting the training of the AI objects corresponding to the individual sub-tasks in the complex task, in parallel at the same time.

For hierarchical decomposition deep reinforcement learning for an artificial intelligence model

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Berkeley, CA, United States of America

Victor Shnayder