Deepmind technologies limited (20240330701). TRAINING AGENT NEURAL NETWORKS THROUGH OPEN-ENDED LEARNING simplified abstract
Contents
TRAINING AGENT NEURAL NETWORKS THROUGH OPEN-ENDED LEARNING
Organization Name
Inventor(s)
Maxwell Elliot Jaderberg of London (GB)
Wojciech Czarnecki of London (GB)
TRAINING AGENT NEURAL NETWORKS THROUGH OPEN-ENDED LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330701 titled 'TRAINING AGENT NEURAL NETWORKS THROUGH OPEN-ENDED LEARNING
Simplified Explanation: The patent application describes methods, systems, and apparatus for training an agent neural network to control an agent in performing various tasks.
Key Features and Innovation:
- Maintaining population data of candidate agent neural networks
- Training each candidate network on specific tasks to update parameter values
- Obtaining data for candidate tasks and control policies
- Training candidate networks based on task relevance
Potential Applications: This technology can be applied in autonomous vehicles, robotics, gaming AI, and industrial automation.
Problems Solved: The technology addresses the need for efficient training of neural networks for diverse tasks and applications.
Benefits:
- Improved performance of agent neural networks
- Enhanced adaptability to different tasks
- Increased efficiency in controlling agents
Commercial Applications: The technology can be utilized in industries such as autonomous vehicles, manufacturing, entertainment, and healthcare robotics.
Prior Art: Researchers can explore prior art related to neural network training methods and applications in robotics and AI.
Frequently Updated Research: Stay updated on advancements in neural network training algorithms, reinforcement learning techniques, and applications in various industries.
Questions about Neural Network Training for Agents: 1. How does this technology improve the efficiency of training agent neural networks? 2. What are the potential challenges in implementing this technology in real-world applications?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for raining an agent neural network for use in controlling an agent to perform a plurality of tasks. one of the methods includes maintaining population data specifying a population of one or more candidate agent neural networks; and training each candidate agent neural network on a respective set of one or more tasks to update the parameter values of the parameters of the candidate agent neural networks in the population data, the training comprising, for each candidate agent neural network: obtaining data identifying a candidate task; obtaining data specifying a control policy for the candidate task; determining whether to train the candidate agent neural network on the candidate task; and in response to determining to train the candidate agent neural network on the candidate task, training the candidate agent neural network on the candidate task.
