Reinforcement Learning Control Parameters for Adaptive Device Operation
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Solution Overview
Problem
Current methods for controlling devices are inadequate in efficiently adjusting control parameters based on real-time data and environmental conditions, leading to suboptimal performance and increased energy consumption.
Innovation Solution
An apparatus and method that utilize reinforcement learning to acquire and adjust control parameters by analyzing measurement data from sensors, using a reward function to optimize control actions, allowing for automatic and data-driven decision-making in controlling devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control methods are used to control devices, then the control system is simple and easy to implement, but the control performance is suboptimal and energy consumption is increased
Solution Approach 1:
The control system performs self-learning through reinforcement learning, where the learning unit automatically optimizes control parameters by receiving rewards or penalties based on control outcomes. This self-service mechanism eliminates the need for manual tuning and enables the system to autonomously improve energy efficiency and control performance over time
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the learning unit receives reward values based on control results, and uses this feedback to continuously update and optimize control parameters. The reward function provides directional guidance for parameter adjustment, enabling the system to learn from past performance and improve future control decisions
2Adaptability or versatility
If trial-and-error operations are used to adjust control parameters, then the system can adapt to different conditions, but the time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary learning during idle periods or off-peak times, accumulating knowledge and optimizing control strategies in advance. This allows the control system to be ready with optimized parameters when actual operational decisions are needed, eliminating the need for time-consuming trial-and-error during critical operations
Solution Approach 2:
The patent replaces manual trial-and-error adjustment with an automated learning system that uses algorithms to optimize control parameters. This substitution of mechanical/manual operations with computational intelligence dramatically reduces the time and effort required for parameter tuning while improving adaptability
Data Source
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AI summary
An apparatus is provided, which includes a first acquisition unit for acquiring measurement data measured by a sensor and a first learning processing unit for executing, by using learning data including the measurement data acquired by the first acquisition unit and a control parameter indicating a first type of control content of at least one device to be controlled, a learning processing of a first model configured to output a recommended control parameter indicating the first type of control content recommended for increasing a reward value determined by a preset reward function in response to input of the measurement data.