Deepmind technologies limited (20240232642). REINFORCEMENT LEARNING USING EPISTEMIC VALUE ESTIMATION simplified abstract
Contents
- 1 REINFORCEMENT LEARNING USING EPISTEMIC VALUE ESTIMATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 REINFORCEMENT LEARNING USING EPISTEMIC VALUE ESTIMATION - 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 Reinforcement Learning Systems
- 1.13 Original Abstract Submitted
REINFORCEMENT LEARNING USING EPISTEMIC VALUE ESTIMATION
Organization Name
Inventor(s)
Hado Philip Van Hasselt of London (GB)
REINFORCEMENT LEARNING USING EPISTEMIC VALUE ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232642 titled 'REINFORCEMENT LEARNING USING EPISTEMIC VALUE ESTIMATION
Simplified Explanation
The patent application describes methods, systems, and apparatus for implementing a control system that selects an action for a reinforcement learning agent based on observations of the environment.
Key Features and Innovation
- Control system selects actions for a reinforcement learning agent based on current state observations.
- Training of the control system is done using a distribution of neural network model parameters derived from a database of previous experiences.
- Utilizes computer programs encoded on a computer storage medium for implementation.
Potential Applications
This technology can be applied in various fields such as robotics, autonomous vehicles, gaming, and industrial automation.
Problems Solved
- Efficient selection of actions for reinforcement learning agents.
- Improved decision-making based on observations of the environment.
- Enhancing the learning process through neural network model parameters.
Benefits
- Enhanced performance of reinforcement learning agents.
- Increased efficiency in decision-making processes.
- Improved adaptability to changing environments.
Commercial Applications
Title: "Optimizing Decision-Making in Reinforcement Learning Systems" This technology can be utilized in industries such as manufacturing, logistics, and healthcare to optimize decision-making processes and improve overall efficiency.
Prior Art
Readers can explore prior research on reinforcement learning, neural networks, and control systems to understand the background of this technology.
Frequently Updated Research
Researchers are continually exploring new ways to enhance reinforcement learning algorithms and improve the efficiency of decision-making processes in dynamic environments.
Questions about Reinforcement Learning Systems
How does this technology improve decision-making processes for reinforcement learning agents?
This technology enhances decision-making by training the control system based on neural network model parameters derived from previous experiences.
What are the potential applications of this technology beyond the fields mentioned in the abstract?
This technology can also be applied in finance, cybersecurity, and natural language processing to optimize decision-making processes and improve overall performance.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a control system of selecting an action to be performed by a reinforcement learning agent, based on an observation characterizing a current state of an environment. the control system is trained based on a distribution of neural network model parameters derived using a database of previous experiences in the environment.
