Volodymyr Mnih

This inventor holds 20 USPTO granted patents and 1 published patent application, plus 1 CIPO patent and 13 EPO patents, primarily in Reinforcement Learning. Top assignees: Deepmind Technologies Limited, Google Inc.. Active years: 2017-2024.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Volodymyr Mnih. Retrieved from https://idiyas.com/inventor/volodymyr-mnih

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 20 (Patent Applications = 1)

% Patents Active = 95.0

CIPO Patents = 1

EPO Patents = 13 (Patent Applications = 8)

Average Co-Inventor Count = 3.5

ph-index = 5

Forward Citations = 99(Granted Patents)

Location History:

London, GB (2017 - 2023)
Toronto, CA (2023 - 2024)

Company Filing History:

18 out of 313

Deepmind Technologies Limited

Years Active: 2017-2024

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Areas of Expertise:

Reinforcement Learning

Q Learning

Actor-Critic

Deep Learning

Unsupervised Control

Image Processing

Neural Networks

Auxiliary Tasks

Training Systems

Aerial Image Labeling

Noise Parameters

Off-Policy Correction

20 patents (USPTO):Sort by Forward CitationsExplore Patents

1. 11868894 - Distributed training using actor-critic reinforcement learning with off-policy correction factors (2024-01-09),

2. 11868866 - Controlling agents using amortized Q learning (2024-01-09),

3. 11842281 - Reinforcement learning with auxiliary tasks (2023-12-12),

4. 11783182 - Asynchronous deep reinforcement learning (2023-10-10),

5. 11727281 - Unsupervised control using learned rewards (2023-08-15),

6. 11593646 - Distributed training using actor-critic reinforcement learning with off-policy correction factors (2023-02-28),

7. 11507827 - Distributed training of reinforcement learning systems (2022-11-22),

8. 11354548 - Image processing with recurrent attention (2022-06-07),

9. 11334792 - Asynchronous deep reinforcement learning (2022-05-17),

10. 11263531 - Unsupervised control using learned rewards (2022-03-01),

11. 11049008 - Reinforcement learning using target neural networks (2021-06-29),

12. 10956820 - Reinforcement learning with auxiliary tasks (2021-03-23),

13. 10936946 - Asynchronous deep reinforcement learning (2021-03-02),

14. 10839293 - Noisy neural network layers with noise parameters (2020-11-17),

15. 10748041 - Image processing with recurrent attention (2020-08-18),

Title: The Innovative Mind of Volodymyr Mnih

Introduction

Volodymyr Mnih, an esteemed inventor and researcher based in London, GB, has made significant contributions to the field of artificial intelligence and reinforcement learning. With a remarkable portfolio of 20 patents, Mnih's work is characterized by cutting-edge methodologies and systems that enhance agent-based interactions with their environments.

Latest Patents

Mnih's latest patents demonstrate his innovative approach to reinforcement learning. One of his notable inventions, titled "Distributed training using actor-critic reinforcement learning with off-policy correction factors," involves methods, systems, and apparatus—including computer programs encoded on a computer storage medium—for training action selection neural networks. This system includes multiple actor computing units generating experience tuples, which feed into learner computing units that utilize off-policy actor-critic techniques to refine learner network parameters.

Another significant patent, "Controlling agents using amortized Q learning," details methods and systems for training a neural network that controls an agent's interactions within an environment. This invention involves processing observations through a proposal neural network to create a probability distribution for potential actions and selecting actions based on Q values generated by a Q neural network.

Career Highlights

Mnih's professional journey includes impactful roles at prominent companies such as DeepMind Technologies Limited and Google Inc. His work at these leading organizations has significantly advanced the state of machine learning and artificial intelligence, establishing him as a thought leader in the field.

Collaborations

Throughout his career, Mnih has collaborated with other distinguished scientists and inventors, including Koray Kavukcuoglu and David Silver. These partnerships reflect a commitment to pushing the boundaries of innovation in artificial intelligence and contributing to groundbreaking research projects.

Conclusion

Volodymyr Mnih's dedication to pushing the boundaries of artificial intelligence through innovative patents and collaborative efforts has solidified his status as a key figure in the tech industry. His pioneering methodologies in reinforcement learning continue to inspire future advancements, making him a significant contributor to the evolution of intelligent systems.

Profile summary based on public USPTO records.