Deepmind technologies limited (20240267532). TRAINING RATE CONTROL NEURAL NETWORKS THROUGH REINFORCEMENT LEARNING simplified abstract

TRAINING RATE CONTROL NEURAL NETWORKS THROUGH REINFORCEMENT LEARNING

Organization Name

deepmind technologies limited

Inventor(s)

Anton Zhernov of London (GB)

Chenjie Gu of Sunnyvale CA (US)

Daniel J. Mankowitz of St. Albans (GB)

Julian Schrittwieser of London (GB)

Amol Balkishan Mandhane of London (GB)

Mary Elizabeth Rauh of London (GB)

Miaosen Wang of Sunnyvale CA (US)

Thomas Keisuke Hubert of London (GB)

TRAINING RATE CONTROL NEURAL NETWORKS THROUGH REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240267532 titled 'TRAINING RATE CONTROL NEURAL NETWORKS THROUGH REINFORCEMENT LEARNING

Simplified Explanation

This patent application describes systems and methods for training rate control neural networks through reinforcement learning. During training, reward values for training examples are generated based on the current and historical performance of the neural network in encoding video.

• Rate control neural networks are trained using reinforcement learning.
• Reward values for training examples are based on the network's performance in encoding video.
• Historical performance of the network is also considered in generating reward values.

Potential Applications

The technology described in this patent application could be applied in various fields such as video encoding, streaming services, and image processing.

Problems Solved

This technology addresses the challenge of optimizing rate control neural networks for efficient video encoding by using reinforcement learning to improve performance.

Benefits

The benefits of this technology include improved video encoding efficiency, better streaming quality, and enhanced image processing capabilities.

Commercial Applications

Title: "Enhanced Video Encoding Technology for Improved Streaming Quality" This technology could be commercially used in video streaming services, video encoding software, and image processing applications to enhance performance and quality.

Prior Art

Readers interested in prior art related to this technology could explore research on neural network training methods, video encoding optimization, and reinforcement learning in video processing.

Frequently Updated Research

Researchers are constantly exploring new methods and algorithms to improve the performance of neural networks in video encoding and processing. Stay updated on the latest advancements in this field for potential breakthroughs.

Questions about Rate Control Neural Networks

What are the key benefits of using reinforcement learning to train rate control neural networks?

Reinforcement learning allows for adaptive training based on performance feedback, leading to improved efficiency and quality in video encoding.

How does the consideration of historical performance impact the training of rate control neural networks?

By incorporating historical performance data, the neural network can learn from past experiences and make more informed decisions during training.

Original Abstract Submitted

systems and methods for training rate control neural networks through reinforcement learning. during training, reward values for training examples are generated from the current performance of the rate control neural network in encoding the video in the training example and the historical performance of the rate control neural network in encoding the video in the training example.