Telefonaktiebolaget lm ericsson (publ) (20240193430). TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT simplified abstract
Contents
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 20240193430 titled 'TECHNIQUE FOR CONFIGURING A REINFORCEMENT LEARNING AGENT
Simplified Explanation: The patent application describes a technique for configuring a reinforcement learning agent to perform a task using a reward structure based on the relative importance of different metrics associated with the task.
- **Key Features and Innovation:**
- Obtaining a definition of metric importances for performance-related metrics. - Deriving a reward structure based on these importances. - Configuring the agent to use the derived reward structure during task performance.
- **Potential Applications:**
- Autonomous systems - Robotics - Gaming industry
- **Problems Solved:**
- Optimizing task performance based on metric importances. - Enhancing the learning process of reinforcement learning agents.
- **Benefits:**
- Improved task performance - Efficient utilization of resources - Enhanced decision-making capabilities
- **Commercial Applications:**
- Optimization of business processes - Personalized user experiences in various industries
- **Prior Art:**
- Prior research on reinforcement learning techniques - Studies on metric importance in task performance
- **Frequently Updated Research:**
- Latest advancements in reinforcement learning algorithms - Studies on the impact of metric importance on task performance
Questions about the Technology: 1. How does this technique improve the efficiency of reinforcement learning agents? 2. What are the potential limitations of using metric importances in defining reward structures for tasks?
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.
