János Kramár

Title: János Kramár: Innovator in Neural Network Training

Introduction

János Kramár is a notable inventor based in London, GB. He has made significant contributions to the field of artificial intelligence, particularly in the training of policy neural networks. His innovative approach has implications for various applications in machine learning and agent control.

Latest Patents

János Kramár holds a patent titled "Training a policy neural network for controlling an agent using best response policy iteration." This patent encompasses methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network by repeatedly updating it at each of a plurality of training iterations. One of the methods involves generating training data for the training iteration by controlling the agent in accordance with an improved policy that selects actions based on input state representations. A best response computation is performed using a candidate policy generated from respective policy neural networks from preceding iterations and a candidate value neural network. The candidate value neural network is designed to generate a value output that estimates the value of the environment in a specific state representation to complete a particular task. The policy neural network is updated by training it on the generated training data.

Career Highlights

János Kramár is associated with Gdm Holding LLC, where he applies his expertise in neural networks and artificial intelligence. His work has positioned him as a key figure in the development of advanced machine learning techniques.

Collaborations

Some of his notable coworkers include Thomas William Anthony and Thomas Edward Eccles. Their collaboration has likely contributed to the innovative projects undertaken at Gdm Holding LLC.

Conclusion

János Kramár's work in training policy neural networks showcases his innovative spirit and dedication to advancing artificial intelligence. His contributions are paving the way for future developments in the field.