# US 2025/0165795 A1 · Reduction of thin volume assemblies using reinforcement learning

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- Number: US 2025/0165795 A1
- Document ID: US20250165795A1
- Status: Published application
- Kind: A1
- Application number: 18/513,711
- Filed: 2023-11-20
- Published: 2025-05-22
- Earliest claimed: 2023-11-20
- Assignees: National Technology & Engineering Solutions of Sandia, LLC
- Applicants: National Technology & Engineering Solutions of Sandia, LLC
- Inventors: Steven J. Owen; Corey Devan Ernst; Armida J. Carbajal; Matthew Gregor Peterson
- Web page: https://patentgrep.com/patent/US20250165795A1

## CPC

- G06N 3/092: Reinforcement learning
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A computer-implemented method for reducing thin volume assemblies and generating sheet bodies using reinforcement learning is provided. The method comprises establishing respective agents for thin volumes and selecting actions for reducing the thin volumes. The method comprises assigning initial rewards to the actions for each agent. The method comprises executing the selected actions by the agents and assigning rewards to the selected actions based on a reward policy. The method comprises continuing the reinforcement learning until at least one stopping criterion is met.

## Claims

### Claim 1

A computer-implemented method for reducing thin volume assemblies and generating sheet bodies using reinforcement learning, comprising:
establishing respective agents for thin volumes;
selecting actions for reducing the thin volumes;
assigning initial rewards to the actions for each agent;
executing the selected actions by the agents;
assigning rewards to the selected actions based on a reward policy; and
continuing the reinforcement learning until at least one stopping criterion is met.

### Claim 2 (depends on 1)

The method of claim 1, wherein the agents are responsible for selecting their actions and maintaining actions' reward history.

### Claim 3 (depends on 1)

The method of claim 1, wherein the actions include a reduce thin copy operation or a mid-surface operation.

### Claim 4 (depends on 1)

The method of claim 1, wherein the initial rewards are calculated using a supervised learning model for the actions.

### Claim 5 (depends on 1)

The method of claim 1, wherein the actions are selected from known actions using a decision policy that allows for exploitation of a state space utilizing supervised learning predictions to exploit knowledge gained from previous iterations of the supervised learning predictions.

### Claim 6 (depends on 1)

The method according to claim 1, wherein stopping criteria include reaching a predefined number of iterations or achieving a specified level of convergence of rewards for the actions.

### Claim 7 (depends on 4)

The method of claim 4, wherein the supervised learning model used for predicting initial rewards employs features including geometry and neighboring thin volumes to improve prediction accuracy over iterations.

### Claim 8 (depends on 1)

The method of claim 1, wherein the actions comprise reduction actions and connection actions, and wherein the reduction actions are performed before the connection actions to ensure thin volume reduction before extending, imprinting and merging neighboring sheet bodies.

### Claim 9 (depends on 1)

The method of claim 1, wherein the actions are assigned rewards based on a reward policy that considers geometric integrity and connection relationships between the thin volumes and neighboring sheet bodies.

### Claim 10

A system for reducing thin volume assemblies and generating sheet bodies using reinforcement learning, the system comprising:
a storage device configured to store program instructions; and
one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
establish respective agents for thin volumes;
select actions for reducing the thin volumes;
assign initial rewards to the actions for each agent;
execute the selected actions by the agents;
assign rewards to the selected actions based on a reward policy; and
continue the reinforcement learning until at least one stopping criterion is met.

### Claim 11 (depends on 10)

The system of claim 10, wherein the agents are responsible for selecting their actions and maintaining actions' reward history.

### Claim 12 (depends on 10)

The system of claim 10, wherein the actions include a reduce thin copy operation or a mid-surface operation.

### Claim 13 (depends on 10)

The system of claim 10, wherein the initial rewards are calculated using a supervised learning model for the actions.

### Claim 14 (depends on 10)

The system of claim 10, wherein the actions are selected from known actions using a decision policy that allows for exploitation of a state space utilizing supervised learning predictions to exploit knowledge gained from previous iterations of the supervised learning predictions.

### Claim 15 (depends on 10)

The system of claim 10, wherein stopping criteria include reaching a predefined number of iterations or achieving a specified level of convergence of rewards for the actions.

### Claim 16 (depends on 14)

The system of claim 14, wherein the supervised learning model used for predicting initial rewards employs features including geometry and neighboring thin volumes to improve prediction accuracy over iterations.

### Claim 17 (depends on 10)

The system of claim 10, wherein the actions comprise reduction actions and connection actions, and wherein the reduction actions are performed before the connection actions to ensure thin volume reduction before extending, imprinting and merging neighboring sheet bodies.

### Claim 18 (depends on 10)

The system of claim 10, wherein the actions are assigned rewards based on a reward policy that considers geometric integrity and connection relationships between the thin volumes and neighboring sheet bodies.

### Claim 19

A computer program product for reducing thin volume assemblies and generating sheet bodies using reinforcement learning, the computer program product comprising:
a computer-readable storage medium having program instructions embodied thereon to perform the steps of:
establishing respective agents for thin volumes;
selecting actions for reducing the thin volumes;
assigning initial rewards to the actions for each agent,
executing the selected actions by the agents;
assigning rewards to the selected actions based on a reward policy; and
continuing the reinforcement learning until at least one stopping criterion is met.

### Claim 20 (depends on 19)

The computer program product of claim 19, wherein the agents are responsible for selecting their actions and maintaining actions' reward history.

### Claim 21 (depends on 19)

The computer program product of claim 19, wherein the actions include a reduce thin copy operation or a mid-surface operation.

### Claim 22 (depends on 19)

The computer program product of claim 19, wherein the initial rewards are calculated using a supervised learning model for the actions.

### Claim 23 (depends on 19)

The computer program product of claim 19, wherein the actions are selected from known actions using a decision policy that allows for exploitation of a state space utilizing supervised learning predictions to exploit knowledge gained from previous iterations of the supervised learning predictions.

### Claim 24 (depends on 19)

The computer program product of claim 19, wherein stopping criteria include reaching a predefined number of iterations or achieving a specified level of convergence of rewards for the actions.

### Claim 25 (depends on 19)

The computer program product of claim 19, wherein the supervised learning model used for predicting initial rewards employs features including geometry and neighboring thin volumes to improve prediction accuracy over iterations.

### Claim 26 (depends on 19)

The computer program product of claim 19, wherein the actions comprise reduction actions and connection actions, and wherein the reduction actions are performed before the connection actions to ensure thin volume reduction before extending, imprinting and merging neighboring sheet bodies.

### Claim 27 (depends on 19)

The computer program product of claim 19, wherein the actions are assigned rewards based on a reward policy that considers geometric integrity and connection relationships between the thin volumes and neighboring sheet bodies.