Google llc (20240220799). CONTROLLING AGENTS USING SCENE MEMORY DATA simplified abstract
Contents
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 20240220799 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 scene memory data.
Key Features and Innovation:
- Utilizes an encoder neural network with a self-attention mechanism to generate an encoded representation of scene memory data.
- Employs a decoder neural network to process the encoded representation and current observation embedding to select actions for the agent.
Potential Applications: This technology can be applied in autonomous vehicles, robotics, gaming AI, and other fields requiring intelligent agent control.
Problems Solved:
- Enhances the decision-making capabilities of agents by effectively processing past observations and current environmental states.
- Improves the efficiency and accuracy of action selection in dynamic environments.
Benefits:
- Enables agents to make informed decisions based on a comprehensive understanding of past and present data.
- Enhances the adaptability and performance of autonomous systems in complex scenarios.
Commercial Applications: The technology can be utilized in industries such as autonomous transportation, industrial automation, and virtual assistants to optimize decision-making processes and enhance overall system performance.
Prior Art: Researchers can explore existing literature on neural network-based agent control systems and attention mechanisms in machine learning to further understand the background of this innovation.
Frequently Updated Research: Stay updated on advancements in neural network architectures, attention mechanisms, and reinforcement learning techniques to enhance the capabilities of agent control systems.
Questions about Agent Control Systems: 1. How does this technology improve the decision-making process of autonomous agents? 2. What are the potential limitations of using neural networks for agent control in dynamic environments?
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.
