Deepmind technologies limited (20240256884). GENERATING ENVIRONMENT MODELS USING IN-CONTEXT ADAPTATION AND EXPLORATION simplified abstract
GENERATING ENVIRONMENT MODELS USING IN-CONTEXT ADAPTATION AND EXPLORATION
Organization Name
Inventor(s)
Hado Philip Van Hasselt of London (GB)
Chentian Jiang of Edinburgh (GB)
GENERATING ENVIRONMENT MODELS USING IN-CONTEXT ADAPTATION AND EXPLORATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256884 titled 'GENERATING ENVIRONMENT MODELS USING IN-CONTEXT ADAPTATION AND EXPLORATION
The patent application describes methods, systems, and apparatus for controlling an agent interacting with an environment to perform a task.
- Maintaining context data
- Receiving a current observation characterizing the current state of the environment
- Generating a current graph model representing the environment
- Selecting a current action from a possible set of actions using the current graph model
- Controlling the agent to perform the selected action to transition the environment to a new state
- Updating the context data with information about the selected action and the new state of the environment
Potential Applications: - Robotics - Autonomous vehicles - Industrial automation
Problems Solved: - Efficient decision-making in dynamic environments - Real-time adaptation to changing conditions
Benefits: - Improved task performance - Enhanced adaptability to environmental changes - Increased efficiency in completing tasks
Commercial Applications: Title: "Advanced Control Systems for Autonomous Agents" This technology can be utilized in industries such as manufacturing, logistics, and transportation to optimize operations and increase productivity.
Questions about the technology: 1. How does this technology improve the efficiency of autonomous agents in performing tasks? 2. What are the key advantages of using a graph model to represent the environment in decision-making processes?
Frequently Updated Research: Stay updated on advancements in artificial intelligence, machine learning, and robotics to understand how they impact the development of control systems for autonomous agents.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent interacting with an environment to perform a task. in one aspect, one of the methods include: maintaining context data; receiving a current observation characterizing a current state of the environment; generating a current graph model that represents the environment; selecting, from a possible set of actions and using the current graph model, a current action to be performed by the agent in response to the current observation; controlling the agent to perform the selected current action to cause the environment to transition from the current state into a new state; and updating the context data to include (i) data identifying the selected current action and (ii) a new observation characterizing the new state of the environment.
