International business machines corporation (20240135234). REINFORCEMENT LEARNING WITH MULTIPLE OBJECTIVES AND TRADEOFFS simplified abstract

REINFORCEMENT LEARNING WITH MULTIPLE OBJECTIVES AND TRADEOFFS

Organization Name

international business machines corporation

Inventor(s)

Radu Marinescu of Dublin (IE)

Parikshit Ram of Atlanta GA (US)

Djallel Bouneffouf of Poughkeepsie NY (US)

Tejaswini Pedapati of White Plains NY (US)

Paulito Palmes of Dublin (IE)

REINFORCEMENT LEARNING WITH MULTIPLE OBJECTIVES AND TRADEOFFS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135234 titled 'REINFORCEMENT LEARNING WITH MULTIPLE OBJECTIVES AND TRADEOFFS

Simplified Explanation

The patent application describes a method for computing possibly optimal policies in reinforcement learning with multiple objectives and tradeoffs. This method involves receiving a dataset containing state, action, and reward information for objectives in a multiple objective environment, as well as tradeoff information indicating preferences between different objective vectors. Based on this data, a set of possibly optimal policies is generated for an intelligent agent operating in the multiple objective environment.

• Simplified Explanation:

 - Method for determining optimal policies in reinforcement learning with multiple objectives and tradeoffs.
 - Dataset with state, action, and reward information is used.
 - Tradeoff information on preferred objective vectors is considered.
 - Set of possibly optimal policies is produced for the intelligent agent.

1. 1. 1. Potential Applications:

- Multi-objective optimization in various fields such as finance, logistics, and robotics. - Decision-making processes in complex systems with conflicting objectives.

1. 1. 1. Problems Solved:

- Balancing multiple objectives in a reinforcement learning environment. - Providing a framework for intelligent agents to make optimal decisions in the face of tradeoffs.

1. 1. 1. Benefits:

- Improved decision-making capabilities in complex environments. - Enhanced efficiency and effectiveness of intelligent agents in achieving multiple objectives simultaneously.

1. 1. 1. Potential Commercial Applications:
         1. Optimizing Multi-Objective Systems

- Utilizing the method to enhance performance in industries like finance, healthcare, and manufacturing.

1. 1. 1. Possible Prior Art:

- Previous research on multi-objective reinforcement learning algorithms. - Existing methods for tradeoff analysis in decision-making processes.

1. 1. 1. 1. Unanswered Questions:
         2. How does this method compare to existing multi-objective reinforcement learning algorithms?

- Answer: This method aims to provide possibly optimal policies by considering tradeoff information, which may differentiate it from traditional approaches that focus solely on maximizing rewards.

1. 1. 1. 1. What real-world applications could benefit the most from this technology?

- Answer: Industries with complex systems and conflicting objectives, such as autonomous vehicles, supply chain management, and financial portfolio optimization, could greatly benefit from this technology.

Original Abstract Submitted

a method for computing possibly optimal policies in reinforcement learning with multiple objectives and tradeoffs includes receiving a dataset comprising state, action, and reward information for objectives in a multiple objective environment. tradeoff information indicating that a first vector comprising first values of the objectives in the multiple objective environment is preferred to a second vector comprising second values of the objectives in the multiple objective environment is received. a set of possibly optimal policies for the multiple objective environment is produced based on the dataset and the tradeoff information, where the set of possibly optimal policies indicates actions for an intelligent agent operating in the multiple objective environment to take.