Telefonaktiebolaget lm ericsson (publ) (20240119300). CONFIGURING A REINFORCEMENT LEARNING AGENT BASED ON RELATIVE FEATURE CONTRIBUTION simplified abstract

CONFIGURING A REINFORCEMENT LEARNING AGENT BASED ON RELATIVE FEATURE CONTRIBUTION

Organization Name

telefonaktiebolaget lm ericsson (publ)

Inventor(s)

Ahmad Ishtar Terra of Sundbyberg (SE)

Rafia Inam of Västerås (SE)

Alberto Hata of CAMPINAS SP (BR)

Ajay Kattepur of BANGALORE (IN)

Hassam Riaz of Järfälla (SE)

CONFIGURING A REINFORCEMENT LEARNING AGENT BASED ON RELATIVE FEATURE CONTRIBUTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119300 titled 'CONFIGURING A REINFORCEMENT LEARNING AGENT BASED ON RELATIVE FEATURE CONTRIBUTION

Simplified Explanation

The abstract describes a computer-implemented method for configuring a reinforcement learning agent to efficiently learn actions in an environment using a machine learning model. The method involves determining actions based on features obtained in the environment, assessing the relative contribution of each feature to the model's decision-making process, and assigning rewards to the agent based on these features.

• The method involves using a machine learning model to determine actions for a reinforcement learning agent based on environmental features.
• It includes evaluating the importance of individual features in influencing the model's decision-making process.
• Rewards given to the agent are determined based on the features and their respective contributions to the model's actions.

Potential Applications

This technology could be applied in various fields such as robotics, autonomous vehicles, game playing agents, and financial trading algorithms.

Problems Solved

This technology helps in improving the efficiency and performance of reinforcement learning agents by better understanding the impact of different features on decision-making.

Benefits

The method allows for more effective training of reinforcement learning agents, leading to better decision-making and performance in various tasks.

Potential Commercial Applications

• "Enhancing Reinforcement Learning Efficiency in Autonomous Vehicles"

Possible Prior Art

One possible prior art could be the use of feature importance analysis in machine learning models to optimize decision-making processes.

Unanswered Questions

How does this method compare to traditional reinforcement learning techniques without feature importance analysis?

This article does not provide a direct comparison between the proposed method and traditional reinforcement learning techniques without feature importance analysis.

What are the potential limitations or challenges in implementing this method in real-world applications?

The article does not address potential limitations or challenges that may arise when implementing this method in practical scenarios.

Original Abstract Submitted

a computer implemented method for configuring a reinforcement learning agent to perform an efficient reinforcement learning procedure, wherein the reinforcement learning agent comprises a model trained using a machine learning process to determine actions to be performed by the reinforcement learning agent. the method comprises using the model to determine an action to perform, based on values of a set of features obtained in an environment; determining, for a first feature in the set of features, a first indication of a relative contribution of the first feature, compared to other features in the set of features, to the determination of the action by the model; and determining a reward to be given to the reinforcement learning agent in response to performing the action, based on the first feature and the first indication.