Abstract: The techniques disclosed herein enable a machine learning model to learn a termination condition of a sub-task. A sub-task is one of a number of sub-tasks that, when performed in sequence, accomplish a long-running task. A machine learning model used to perform the sub-task is augmented to also provide a termination signal. The termination signal indicates whether the sub-task's termination condition has been met. Monitoring the termination signal while performing the sub-task enables subsequent sub-tasks to seamlessly begin at the appropriate time. A termination condition may be learned from the same data used to train other model outputs. In some configurations, the model learns whether a sub-task is complete by periodically attempting subsequent sub-tasks. If a subsequent sub-task can be performed, positive reinforcement is provided for the termination condition. The termination condition may also be trained using synthetic scenarios designed to test when the termination condition has been met.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.