Computer Platform for Human-in-the-Loop Reinforcement Learning
AssigneeGoogle Inc.
InventorsAmit Mohan Joshi, Yilun Liu, Phillip Edward Dieter Maier
Life of this patent
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Abstract
A computer-implemented method includes receiving information associated with one or more machine-learned models provided by an external computing system, providing predefined code segments for obtaining human-in-the-loop reinforcement learning (HITL RL) feedback with respect to the one or more machine-learned models, identifying a selection of one or more of the predefined code segments, and generating a HITL RL framework configured to be applied to the one or more machine-learned models to obtain feedback associated with performance of the one or more machine-learned models, based on the selection of the one or more of the predefined code segments.
Claims20
A computer-implemented method, comprising:
- receiving, by a computing system, information associated with one or more machine-learned models provided by an external computing system;
- providing, by the computing system, predefined code segments for obtaining human-in-the-loop reinforcement learning (HITL RL) feedback with respect to the one or more machine-learned models;
- identifying, by the computing system, a selection of one or more of the predefined code segments;
- and
- generating, by the computing system, a HITL RL framework configured to be applied to the one or more machine-learned models to obtain feedback associated with performance of the one or more machine-learned models, based on the selection of the one or more of the predefined code segments.
A computing system, comprising:
- one or more processors;
- and
- one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
- receiving information associated with one or more machine-learned models provided by an external computing system;
- providing predefined code segments for obtaining human-in-the-loop reinforcement learning (HITL RL) feedback with respect to the one or more machine-learned models;
- identifying a selection of one or more of the predefined code segments;
- and
- generating a HITL RL framework configured to be applied to the one or more machine-learned models to obtain feedback associated with performance of the one or more machine-learned models, based on the selection of the one or more of the predefined code segments.
A non-transitory computer-readable medium which stores instructions that are executable by one or more processors of a computing system, the instructions causing the one or more processors to perform operations, the operations comprising:
- receiving information associated with one or more machine-learned models provided by an external computing system;
- providing predefined code segments for obtaining human-in-the-loop reinforcement learning (HITL RL) feedback with respect to the one or more machine-learned models;
- identifying a selection of one or more of the predefined code segments;
- and
- generating a HITL RL framework configured to be applied to the one or more machine-learned models to obtain feedback associated with performance of the one or more machine-learned models, based on the selection of the one or more of the predefined code segments.
Dependencies are inferred from the claim text.