18275580. CONFIGURING A REINFORCEMENT LEARNING AGENT BASED ON RELATIVE FEATURE CONTRIBUTION simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

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 18275580 titled 'CONFIGURING A REINFORCEMENT LEARNING AGENT BASED ON RELATIVE FEATURE CONTRIBUTION

Simplified Explanation

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

• Explanation of the patent:

 * The method configures a reinforcement learning agent to learn actions efficiently in an environment.
 * It uses a trained model to determine actions based on features obtained in the environment.
 * The method assesses the relative contribution of each feature to the model's decision-making process.
 * Rewards are assigned to the agent based on the features and their contributions to the actions taken.

Potential Applications

This technology could be applied in various fields such as robotics, autonomous vehicles, gaming, and financial trading where efficient decision-making processes are crucial.

Problems Solved

• Efficient learning: The method helps the reinforcement learning agent to learn actions efficiently by assessing the contribution of each feature to the decision-making process.
• Optimal decision-making: By assigning rewards based on features and their contributions, the agent can make more informed and optimal decisions in the environment.

Benefits

• Improved performance: By configuring the agent to learn efficiently and make optimal decisions, overall performance can be enhanced.
• Enhanced adaptability: The method allows the agent to adapt to different environments and tasks by assessing feature contributions.

Potential Commercial Applications

Optimizing decision-making processes in industries such as finance, healthcare, and manufacturing can lead to increased efficiency and cost savings.

Possible Prior Art

One possible prior art in this field is the use of feature importance techniques in machine learning models to understand the impact of different features on the model's predictions. This method builds upon such techniques by specifically applying them to reinforcement learning agents for efficient learning and decision-making processes.

How does this method compare to traditional reinforcement learning techniques?

This article does not directly compare the proposed method to traditional reinforcement learning techniques in terms of performance, efficiency, or scalability.

What are the potential limitations of this method in real-world applications?

The article does not address the potential challenges or limitations that may arise when implementing this method in complex real-world environments or 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.