18536074. CONTROLLING AGENTS USING SCENE MEMORY DATA simplified abstract (Google LLC)
Contents
- 1 CONTROLLING AGENTS USING SCENE MEMORY DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTROLLING AGENTS USING SCENE MEMORY DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Neural Network-Based Agent Control Systems
- 1.13 Original Abstract Submitted
CONTROLLING AGENTS USING SCENE MEMORY DATA
Organization Name
Inventor(s)
Alexander Toshkov Toshev of San Francisco CA (US)
CONTROLLING AGENTS USING SCENE MEMORY DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18536074 titled 'CONTROLLING AGENTS USING SCENE MEMORY DATA
Simplified Explanation
The patent application describes methods, systems, and apparatus for controlling an agent using neural networks to process observations and generate action selections.
Key Features and Innovation
- Utilizes an encoder neural network with a self-attention mechanism to process scene memory data.
- Employs a decoder neural network to generate action selection outputs based on the processed data.
- Enables the agent to perform selected actions based on the generated outputs.
Potential Applications
This technology could be applied in autonomous vehicles, robotics, gaming AI, and other fields requiring intelligent agent control.
Problems Solved
- Enhances the efficiency and accuracy of agent control systems.
- Improves decision-making processes for agents operating in dynamic environments.
Benefits
- Increases the adaptability and responsiveness of agents.
- Enhances overall performance and effectiveness in various applications.
Commercial Applications
- "Neural Network-Based Agent Control System for Autonomous Vehicles and Robotics"
- This technology could revolutionize the way autonomous systems operate, leading to safer and more efficient transportation solutions.
Prior Art
Prior research in the field of neural network-based agent control systems can be found in academic journals and conferences related to artificial intelligence and machine learning.
Frequently Updated Research
Researchers are continually exploring advancements in neural network architectures and algorithms for agent control systems, leading to ongoing improvements in performance and capabilities.
Questions about Neural Network-Based Agent Control Systems
How do neural networks improve agent control systems?
Neural networks enhance agent control systems by processing complex data inputs and generating optimized action outputs based on learned patterns and relationships.
What are the potential limitations of neural network-based agent control systems?
The limitations of neural network-based agent control systems may include computational complexity, training data requirements, and potential biases in decision-making processes.
Original Abstract Submitted
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes receiving a current observation characterizing a current state of the environment as of the time step; generating an embedding of the current observation; processing scene memory data comprising embeddings of prior observations received at prior time steps using an encoder neural network, wherein the encoder neural network is configured to apply an encoder self-attention mechanism to the scene memory data to generate an encoded representation of the scene memory data; processing the encoded representation of the scene memory data and the embedding of the current observation using a decoder neural network to generate an action selection output; and causing the agent to perform the selected action.
