Thomas Rothoerl

Title: Thomas Rothoerl: Innovator in Neural Network Systems

Introduction

Thomas Rothoerl is a prominent inventor based in London, GB. He has made significant contributions to the field of artificial intelligence, particularly in the development of neural network systems. With a total of four patents to his name, Rothoerl is recognized for his innovative approaches to machine learning and reinforcement learning.

Latest Patents

One of Rothoerl's latest patents is titled "Training action selection neural networks using apprenticeship." This invention describes an off-policy reinforcement learning actor-critic neural network system. The system is designed to select actions from a continuous action space, enabling an agent to interact with an environment to perform specific tasks. The architecture includes an actor neural network that learns a policy function, mapping state data to action data, and a critic neural network that learns an action-value (Q) function. Additionally, a replay buffer is utilized to store tuples of state data, action data, reward data, and new state data, which facilitates the training of both the actor and critic networks off-policy.

Career Highlights

Rothoerl is currently employed at DeepMind Technologies Limited, a leading company in artificial intelligence research. His work focuses on advancing the capabilities of neural networks, contributing to the development of intelligent systems that can learn and adapt in complex environments.

Collaborations

Throughout his career, Thomas Rothoerl has collaborated with notable colleagues, including Mel Vecerik and Nicolas Manfred Otto Heess. These collaborations have further enriched his research and innovation in the field of machine learning.

Conclusion

Thomas Rothoerl stands out as a key figure in the realm of neural network innovations. His patents and contributions to artificial intelligence continue to shape the future of technology.