Tu-Hoa Pham

Title: Tu-Hoa Pham: Innovator in Reinforcement Learning

Introduction

Tu-Hoa Pham is a notable inventor based in Koto, Japan. She has made significant contributions to the field of reinforcement learning, holding a total of five patents. Her work focuses on developing methods that enhance the efficiency and safety of reinforcement learning processes.

Latest Patents

Tu-Hoa Pham's latest patents include "Safe and fast exploration for reinforcement learning using constrained action manifolds." This invention provides a computer-implemented method for reinforcement learning, which involves reading an action manifold described as an n-polytope, physical action limits, and safety constraints. The method updates the action manifold based on these limits and constraints, allowing for the selection of constrained actions during reinforcement learning. Another significant patent is "Sequential learning of constraints for hierarchical reinforcement learning." This invention outlines a method for Hierarchical Reinforcement Learning (HRL) that includes obtaining a sequence of tasks based on hierarchical relations and learning constraints corresponding to these tasks through reinforcement and supervised learning.

Career Highlights

Tu-Hoa Pham is currently employed at International Business Machines Corporation, commonly known as IBM. Her work at IBM has allowed her to explore innovative solutions in the realm of artificial intelligence and machine learning.

Collaborations

Tu-Hoa has collaborated with notable colleagues, including Giovanni De De Magistris and Ryuki Tachibana. These collaborations have contributed to her success and the advancement of her research in reinforcement learning.

Conclusion

Tu-Hoa Pham is a pioneering inventor whose work in reinforcement learning has the potential to transform the field. Her innovative patents and contributions to IBM highlight her role as a leader in technological advancements.