Automatic navigation of interactive web documents
AssigneeGoogle Inc.
InventorsAleksandra Faust, Dilek Hakkani-Tur, Izzeddin Gur, Ulrich Rueckert
Life of this patent
Estimating the term…
Loading patent…
AssigneeGoogle Inc.
InventorsAleksandra Faust, Dilek Hakkani-Tur, Izzeddin Gur, Ulrich Rueckert
Estimating the term…
The present disclosure is generally directed to methods, apparatus, and computer-readable media (transitory and non-transitory) for learning to automatically navigate interactive web documents and/or websites. More particularly, various approaches are presented for training various deep Q network (DQN) agents to perform various tasks associated with reinforcement learning, including hierarchical reinforcement learning, in challenging web navigation environments with sparse rewards and large state and action spaces. These agents include a web navigation agent that can use learned value function(s) to automatically navigate through interactive web documents, as well as a training agent, referred to herein as a “meta-trainer,” that can be trained to generate synthetic training examples. Some approaches described herein may be implemented when expert demonstrations are available. Other approaches described herein may be implemented when expert demonstrations are not available. In either case, dense, potential-based rewards may be used to augment the training.
A method implemented using one or more processors, comprising:
A method implemented using one or more processors, comprising:
A system comprising one or more processors and memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to:
Dependencies are inferred from the claim text.