Company Filing History:
Years Active: 2020-2025
Title: Innovations by Tom Erez in Deep Reinforcement Learning
Introduction: Tom Erez is an accomplished inventor based in London, GB, known for his contributions in the field of artificial intelligence, specifically in deep reinforcement learning. With a total of two patents to his name, Erez has made significant strides in developing systems and methods that enhance agent-environment interactions through advanced neural network techniques.
Latest Patents: One of Tom Erez's latest patents focuses on "Continuous control with deep reinforcement learning." This patent outlines methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training an actor neural network that selects actions for an agent interacting with an environment. The methods described involve obtaining a minibatch of experience tuples and updating the parameters of the actor neural network. This includes processing training observations and actions through a critic neural network to determine outputs, along with updating the critic's parameters based on errors between output values. Through these innovative approaches, Erez's work aims to advance the performance and capabilities of agents in dynamic environments.
Career Highlights: Tom Erez has established an impactful career working with DeepMind Technologies Limited, a leading company in artificial intelligence research and development. His expertise in deep reinforcement learning has played a crucial role in the company's ongoing projects, furthering the understanding of AI's capabilities in complex scenarios.
Collaborations: Throughout his career, Erez has collaborated with notable figures in the field, including Timothy Paul Lillicrap and Jonathan James Hunt. These collaborations have contributed to the advancement of research and the development of innovative patentable technologies.
Conclusion: Tom Erez stands out as a key inventor within the realm of artificial intelligence, particularly in deep reinforcement learning. His patents not only reflect his innovative approach but also contribute to the broader understanding of how agents can be trained to operate more efficiently in their environments. As his work continues to evolve, it sets a promising foundation for future advancements in AI technology.