View source for 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==

[[:Category:telefonaktiebolaget lm ericsson (publ)|telefonaktiebolaget lm ericsson (publ)]]


[[Category:telefonaktiebolaget lm ericsson (publ)]]

==Inventor(s)==

[[:Category:Ahmad Ishtar Terra of Sundbyberg (SE)|Ahmad Ishtar Terra of Sundbyberg (SE)]][[Category:Ahmad Ishtar Terra of Sundbyberg (SE)]]

[[:Category:Rafia Inam of Västerås (SE)|Rafia Inam of Västerås (SE)]][[Category:Rafia Inam of Västerås (SE)]]

[[:Category:Alberto Hata of CAMPINAS SP (BR)|Alberto Hata of CAMPINAS SP (BR)]][[Category:Alberto Hata of CAMPINAS SP (BR)]]

[[:Category:Ajay Kattepur of BANGALORE (IN)|Ajay Kattepur of BANGALORE (IN)]][[Category:Ajay Kattepur of BANGALORE (IN)]]

[[:Category:Hassam Riaz of Järfälla (SE)|Hassam Riaz of Järfälla (SE)]][[Category: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.



[[Category:G06N3/092]]