18539263. ADAPTATION SYSTEM AND ADAPTATION METHOD simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
ADAPTATION SYSTEM AND ADAPTATION METHOD
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Akihiro Katayama of Toyota-shi (JP)
Kenichiro Kumada of Nagakute-shi (JP)
ADAPTATION SYSTEM AND ADAPTATION METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18539263 titled 'ADAPTATION SYSTEM AND ADAPTATION METHOD
Simplified Explanation: The patent application describes a system that performs a second process after a learning routine has been executed a certain number of times. This second process involves conducting trials and recording changes in rewards. Once the learning routine reaches a specified number of executions, a third process calculates summary statistics and updates a control map for optimization.
Key Features and Innovation:
- System executes a second process after a learning routine has been executed a certain number of times.
- Second process involves conducting trials and recording changes in rewards.
- Third process calculates summary statistics and updates a control map for optimization.
Potential Applications: This technology could be applied in various fields such as artificial intelligence, machine learning, robotics, and automation.
Problems Solved: This technology addresses the need for efficient learning routines and optimization processes in complex systems.
Benefits:
- Improved learning efficiency
- Enhanced optimization capabilities
- Better decision-making based on recorded rewards
Commercial Applications: Potential commercial applications include AI systems, autonomous vehicles, industrial automation, and smart technologies for various industries.
Prior Art: Researchers can explore prior art related to reinforcement learning, control systems, and optimization algorithms in the field of artificial intelligence.
Frequently Updated Research: Stay updated on advancements in reinforcement learning algorithms, control theory, and optimization techniques for complex systems.
Questions about the Technology: 1. How does this technology improve decision-making processes in complex systems? 2. What are the potential limitations of this system in real-world applications?
Original Abstract Submitted
Processing circuitry of an adaptation system executes a second process when a number of times of execution of a learning routine is greater than or equal to a specified number of times and less than a termination number of times. The second process performs a first trial and a second trial in each execution of the learning routine, and ends the learning routine by recording, in a storage device, a change in one of the first trial and the second trial in which a reward is larger. The processing circuitry executes a third process when the number of times of execution of the learning routine reaches a specified number of times. The third process is a process of calculating summary statistics of multiple changes and reflecting the summary statistics in a control map to complete optimization of the control map.
