Control Strategy Optimization with Adaptive Bayesian Parameter Bounds
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Solution Overview
Problem
Conventional methods for optimizing control models in reinforcement learning are inefficient due to the need for numerous measurement operations and long training times, especially when dealing with large numbers of model parameters and noisy data, and often result in suboptimal results due to unsuitable model structures.
Innovation Solution
A Bayesian optimization method is employed to iteratively optimize model parameters by expanding the permissible value ranges for model parameters that lie at the boundary of their domains, using a quality function modeled as a Gaussian process regression to balance exploration and exploitation, thereby reducing the number of measurements required and improving convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model-free reinforcement learning methods are used to optimize control strategies, then no knowledge of the environment is necessary, but the system's interaction time with the environment during the learning process is very long
Solution Approach 1:
The patent applies preliminary action by pre-defining a model structure that describes the control system's behavior before optimization begins. This model structure is created in advance based on domain knowledge, allowing the optimization process to work within a predefined framework rather than exploring all possible models from scratch, thus reducing learning time while maintaining adaptability through parameter optimization.
Solution Approach 2:
The patent optimizes control strategies by adjusting model parameters within the predefined structure. Instead of changing the entire model structure or learning from scratch in the environment, the system performs parameter optimization on an existing model, significantly reducing the interaction time required while maintaining the ability to adapt to different environments through parameter tuning.
2Productivity
If conventional model-based methods are used with parameter adaptation, then optimization can be carried out efficiently, but the selected model structure may be unsuitable and the parameter adaptation may not yield an optimal result
Solution Approach 1:
The patent introduces dynamics by making the model structure itself adaptable through iterative optimization. The model structure is not fixed but can be modified based on optimization results, allowing the system to transition from a static predefined structure to a dynamic structure that evolves during the optimization process to better suit the specific application.
Solution Approach 2:
The patent implements feedback mechanisms where the performance of the control model is continuously evaluated during optimization, and this feedback is used to guide further parameter adjustments and potential structural modifications. The cost function provides feedback on optimization quality, and the iterative process uses this feedback to improve both parameters and structure, ensuring high optimization quality while maintaining efficiency.
3Ease of manufacture
If Bayesian optimization is used to create control models with large numbers of model parameters, then efficient black-box optimization is possible, but a large number of measurement operations are necessary and long training times are the rule
Solution Approach 1:
The patent applies segmentation by dividing the optimization process into distinct phases: initial model structure definition, parameter optimization phase, and iterative refinement phase. This segmentation allows the system to handle complex models with many parameters more efficiently by breaking down the optimization task into manageable stages, reducing overall training time while maintaining the ease of creating control models through Bayesian optimization.
Data Source
AI summary
A computer-implemented method for creating a control process for a technical system using a Bayesian optimization method, the control process being created and executable based on model parameters of a control model, the following steps being performed in order to optimize the control process: furnishing a quality function that corresponds to a trainable regression function, and that assesses a quality of a control process of the technical system based on model parameters; executing a Bayesian optimization method based on the quality function in order to iteratively ascertain an optimized model parameter set having model parameters, such that during execution of the Bayesian optimization method, a model parameter domain that indicates the permissible value ranges for the model parameters is expanded, by an amount equal to an expansion distance, with respect to those dimensions for which the model parameter ascertained in the current iteration lies at a range boundary.