CAD-Based Reinforcement Learning Environment Setup
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Solution Overview
Problem
Reinforcement learning in artificial intelligence faces challenges in optimizing the location of a target object due to differences between actual and simulated environments, making it difficult to customize and apply reinforcement learning environments effectively, especially in manufacturing processes where the learned actions are not optimized.
Innovation Solution
A user learning environment-based reinforcement learning apparatus and method that utilizes a simulation engine to set a customized reinforcement learning environment by analyzing design data, including object and location information, and providing feedback through reward information to optimize the disposition of a target object, allowing for efficient configuration and optimization of the target object's location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a virtual environment is created to imitate the actual environment for reinforcement learning, then the learning can be performed automatically, but the cost (time and labor) to produce the virtual environment becomes very high
Solution Approach 1:
The patent uses CAD data (computer-aided design data) to automatically generate the virtual environment, effectively copying the actual manufacturing environment from digital design files. This eliminates the need for manual creation of virtual environments while maintaining accuracy, directly resolving the contradiction between automation and time consumption.
2Manufacturing precision
If the virtual environment is customized to match the actual environment, then the learning accuracy improves, but the complexity of setting up the environment increases
Solution Approach 1:
By copying the actual environment directly from CAD data, the system achieves high learning accuracy without manual customization. The CAD data already contains precise geometric and spatial information, so the virtual environment inherits this accuracy automatically, eliminating the need for complex setup procedures.
Solution Approach 2:
The system performs automatic environment generation from CAD data without requiring manual intervention for customization. The reinforcement learning apparatus itself handles the environment setup process, reducing the complexity burden on users while maintaining high fidelity to the actual environment.
3Ease of manufacture
If the virtual environment is designed to be simple and easy to create, then the setup cost decreases, but the difference between the actual and virtual environments increases, reducing learning effectiveness
Solution Approach 1:
The patent copies the actual environment directly from CAD data, which serves as the authoritative source for the manufacturing process. This approach simultaneously achieves ease of creation (automatic generation from existing data) and high fidelity (direct representation of actual environment), resolving the contradiction between these two requirements.
4Device complexity
If reinforcement learning is performed with a fixed environment configuration, then the learning process is simpler, but the learned actions are not optimized when applied to varying actual environments
Solution Approach 1:
The system dynamically adapts the virtual environment by loading different CAD data to reflect varying actual manufacturing environments. This allows the reinforcement learning agent to learn from environment-specific configurations while maintaining a consistent learning framework, achieving both simplicity and adaptability.
Solution Approach 2:
The system changes environmental parameters by loading different CAD data that represents different actual environments. This allows the virtual environment to be reconfigured for different manufacturing scenarios without changing the fundamental learning architecture, maintaining simplicity while improving adaptability.
Data Source
AI summary
Disclosed is a user learning environment-based reinforcement learning apparatus and method. According to the disclosure, a CAD data based-reinforcement learning environment may be easily set by a user using a user interface (UI) and a drag and drop, a reinforcement learning environment may be promptly configured, and reinforcement learning may be performed based on the learning environment set by the user, and thus the optimized location of a target object may be automatically produced in various environments.