Bayesian Control Parameter Search With Learned Acquisition Functions
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Solution Overview
Problem
Existing Bayesian optimization methods for technical systems rely on manually defined acquisition functions, which can lead to inefficient searches, particularly in high-dimensional problems, and require extensive computing time, limiting their effectiveness in finding functional optima with minimal tests.
Innovation Solution
Automatically determining acquisition functions using reinforcement learning or imitation learning within a simulation environment, allowing for iterative optimization and adaptation based on process quality, resulting in more efficient and faster evaluation of test points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manually defined acquisition functions are used in Bayesian optimization, then the method can be implemented with simple manual configuration, but the search efficiency is poor and computing time is extensive
Solution Approach 1:
The system automatically determines acquisition functions using reinforcement learning or imitation learning, enabling the optimization process to self-improve without manual intervention. The learning method iteratively optimizes the acquisition function based on feedback from simulation results, allowing the system to adapt to specific problem classes autonomously.
Solution Approach 2:
The invention changes the acquisition function parameters dynamically through learning processes. Instead of using fixed manually-defined acquisition functions, the system learns and adapts the acquisition function parameters based on problem-specific characteristics, thereby improving search efficiency for different problem types.
2Ease of operation
If manually defined acquisition functions are used in Bayesian optimization, then the implementation is straightforward, but the computing time is extensive
Solution Approach 1:
The system performs preliminary learning in a simulation environment before actual optimization. The acquisition function is trained offline using reinforcement learning or imitation learning on simulated data, so that when deployed on real technical systems, it requires minimal computing time during the actual optimization process.
Solution Approach 2:
The invention uses simulation copies of the technical system to train the acquisition function. By learning from simulated environments that replicate real system behavior, the system avoids extensive computing time on actual hardware while still achieving effective optimization strategies.
3Device complexity
If conventional acquisition functions are used, then the method is simple to implement, but fewer test points cannot be achieved
Solution Approach 1:
The learning method automatically adapts the acquisition function to the specific problem class through self-learning in simulation environments. This self-service capability allows the system to achieve higher optimization precision without requiring complex manual configuration or extensive test points.
Solution Approach 2:
The invention dynamically adjusts acquisition function parameters based on learned problem characteristics. By changing the acquisition function parameters according to specific problem types, the system achieves better optimization precision with fewer test points compared to conventional fixed acquisition functions.
Data Source
Figure 1
AI summary
A method for controlling or regulating a technical system is presented, in which a parameter of the control or regulation is determined by Bayesian optimization, and a next test point is determined by an acquisition function within the Bayesian optimization. The acquisition function is automatically determined by a learning algorithm within the Bayesian optimization, the parameter of the control or regulation is determined using this acquisition function, and the technical system is controlled or regulated based on the determined parameter.