Toyota jidosha kabushiki kaisha (20240300470). ADAPTATION SYSTEM AND ADAPTATION METHOD simplified abstract
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 20240300470 titled 'ADAPTATION SYSTEM AND ADAPTATION METHOD
Simplified Explanation: An adaptation system's processing circuitry conducts trials based on rewards, updating a control map until a condition is met, then repeats trials to compare rewards and update the map.
Key Features and Innovation:
- Processing circuitry executes trials based on rewards in a learning routine.
- Control map reflects changes in trials with larger rewards.
- Two distinct processes based on the number of executions of trials.
- Comparison of rewards for multiple executions to determine the trial with the larger reward.
Potential Applications: This technology could be applied in machine learning systems, reinforcement learning algorithms, autonomous systems, and adaptive control systems.
Problems Solved: This technology addresses the need for efficient learning routines that adapt based on rewards and optimize decision-making processes.
Benefits:
- Improved learning efficiency.
- Enhanced decision-making capabilities.
- Adaptive control in dynamic environments.
Commercial Applications: Potential commercial applications include autonomous vehicles, robotics, gaming AI, and smart home systems.
Prior Art: Prior research in reinforcement learning, adaptive control systems, and machine learning algorithms may provide insights into similar approaches.
Frequently Updated Research: Stay informed about advancements in reinforcement learning algorithms, adaptive control systems, and machine learning techniques.
Questions about the Technology: 1. How does this technology improve decision-making processes in autonomous systems? 2. What are the implications of using this technology in adaptive control systems?
1. A relevant generic question not answered by the article, with a detailed answer. - How does this technology compare to traditional reinforcement learning algorithms? This technology differs by dynamically updating trials based on rewards, optimizing decision-making processes in real-time.
2. Another relevant generic question, with a detailed answer. - What are the potential challenges in implementing this technology in complex systems? Implementing this technology may require robust processing capabilities and efficient data management to handle multiple executions of trials effectively.
Original Abstract Submitted
until a specified condition is met, a processing circuitry of an adaptation system executes a first trial and a second trial in each execution of a learning routine, reflects, in a control map, a change in the trial with a larger reward, and ends the learning routine (first process). after the specified condition is met, the processing circuitry executes the first trial and the second trial multiple times in each learning routine, compares the reward for the multiple executions of the first trial with the reward for the multiple executions of the second trial, and reflects, in a control map, a change in the trial with the larger reward, and ends the learning routine (second process).
