Ulrich Rueckert

Title: Innovations of Ulrich Rueckert

Introduction

Ulrich Rueckert is an accomplished inventor based in San Francisco, CA. He has made significant contributions to the field of technology, particularly in the area of web navigation. With a total of 2 patents to his name, Rueckert's work focuses on enhancing user interaction with web documents.

Latest Patents

One of Rueckert's latest patents is titled "Automatic navigation of interactive web documents." This patent outlines methods, apparatus, and computer-readable media for learning to automatically navigate interactive web documents and websites. The patent presents various approaches for training deep Q network (DQN) agents to perform tasks associated with reinforcement learning. These tasks include hierarchical reinforcement learning in challenging web navigation environments characterized by sparse rewards and large state and action spaces. The agents described in this patent include a web navigation agent that utilizes learned value functions to navigate through interactive web documents, as well as a training agent known as a 'meta-trainer' that generates synthetic training examples. The approaches can be implemented with or without expert demonstrations, and dense, potential-based rewards may be used to enhance the training process.

Career Highlights

Ulrich Rueckert is currently employed at Google Inc., where he continues to innovate and develop new technologies. His work at Google has allowed him to explore advanced concepts in web navigation and artificial intelligence.

Collaborations

Rueckert collaborates with notable colleagues, including Aleksandra Faust and Dilek Zeynep Hakkani-Tur. These collaborations contribute to the advancement of technology and the development of innovative solutions in their respective fields.

Conclusion

Ulrich Rueckert's contributions to the field of technology, particularly in web navigation, demonstrate his innovative spirit and commitment to enhancing user experiences. His patents reflect a deep understanding of reinforcement learning and its applications in real-world scenarios.