18286609. TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT simplified abstract (Telefonaktiebolaget LM Ericsson (PUBL))
Contents
- 1 TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT - 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 Task-Specific Reinforcement Learning Agent Configuration
- 1.13 Original Abstract Submitted
TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT
Organization Name
Telefonaktiebolaget LM Ericsson (PUBL)
Inventor(s)
Ajay Kattepur of Bangalore (IN)
Ahmad Ishtar Terra of Sundbyberg (SE)
Alberto Hata of Barueri SP (BR)
Prayag Gowgi Somanahalli Krishna Murthy of Mandya (IN)
TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18286609 titled 'TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT
Simplified Explanation
This patent application describes a technique for configuring a reinforcement learning agent to perform a task based on a reward structure derived from the importance of specific metrics related to the task.
- The method involves obtaining importance values for various performance metrics, creating a reward structure based on these values, and configuring the agent to use this structure during task execution.
Key Features and Innovation
- Configuring a reinforcement learning agent based on task-specific metric importances.
- Deriving a reward structure from the importance values of performance metrics.
- Enhancing the agent's performance by attributing rewards based on metric importance.
Potential Applications
This technology can be applied in various fields such as robotics, autonomous vehicles, and game playing where reinforcement learning agents are used to perform tasks efficiently.
Problems Solved
- Provides a method to prioritize performance metrics in reinforcement learning.
- Enhances the agent's decision-making process by focusing on important metrics.
- Improves task performance by aligning rewards with metric importance.
Benefits
- Optimizes the performance of reinforcement learning agents.
- Increases efficiency and effectiveness in task execution.
- Enables better adaptation to changing task requirements.
Commercial Applications
Title: "Task-Specific Reinforcement Learning Agent Configuration" This technology can be utilized in industries such as manufacturing, healthcare, and finance to automate processes, optimize resource allocation, and improve decision-making based on task-specific requirements.
Prior Art
Further research can be conducted in the fields of reinforcement learning, machine learning, and artificial intelligence to explore similar techniques for configuring agents based on metric importance.
Frequently Updated Research
Stay updated on advancements in reinforcement learning algorithms, task-specific optimization methods, and applications of metric importance in agent configuration.
Questions about Task-Specific Reinforcement Learning Agent Configuration
How does this technology improve the performance of reinforcement learning agents?
This technology enhances agent performance by prioritizing important metrics and aligning rewards accordingly.
What are the potential applications of this technique beyond the ones mentioned in the patent application?
This technique can be applied in various industries such as logistics, energy management, and cybersecurity to optimize processes and decision-making based on task-specific requirements.
Original Abstract Submitted
A technique for configuring a reinforcement learning agent to perform a task using a reward structure derived from a task-specific definition of metric importances is disclosed. A method is performed by a computing unit executing a configurator component and includes obtaining a definition of metric importances specifying, for a plurality of performance-related metrics associated with the task, pairwise importance values each indicating a relative importance of one metric with respect to another metric of the plurality of performance-related metrics for the task, deriving a reward structure from the definition of metric importances, the reward structure defining, for each of the plurality of performance-related metrics, a reward to be attributed to an action taken by the reinforcement learning agent that yields a positive outcome in the respective performance-related metric, and configuring the reinforcement learning agent to employ the derived reward structure when performing the task.
