Command Delivery Scheduling Using Reinforcement Learning
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Solution Overview
Problem
Existing systems face performance degradation due to excessive and simultaneous access of applications to control devices, leading to delays and slow operations, which conventional methods like Patent Document 1 fail to address effectively.
Innovation Solution
A machine learning device that performs reinforcement learning to optimize the delivery schedule of commands to a control device by monitoring and adjusting the delivery schedule of commands based on state data, using Q-learning to minimize delivery delays and prevent overloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple applications frequently access data in the control device simultaneously, then the data access frequency increases, but the control device becomes overloaded and performance degrades
Solution Approach 1:
The machine learning device performs preliminary analysis of command patterns and delivery schedules before actual command execution. By predicting future command volumes and timing, the system proactively adjusts delivery schedules to prevent control device overload before it occurs, rather than reactively responding to performance degradation
Solution Approach 2:
The system implements a feedback mechanism where the machine learning device continuously monitors control device performance metrics and command delivery outcomes. This feedback loop enables dynamic adjustment of command delivery schedules, allowing the system to learn from past performance and optimize future command timing to maintain control device reliability while sustaining high data access frequency
2Speed
If command delivery is optimized to reduce delays, then the delivery speed increases, but the control device may become overloaded
Solution Approach 1:
The command delivery schedule is made dynamic rather than static. The machine learning device continuously adapts the delivery timing and intervals based on real-time control device performance and learned patterns. This dynamic adjustment allows the system to increase delivery speed when the control device can handle it, while automatically slowing down delivery to prevent overload when capacity is limited
Solution Approach 2:
The system changes key parameters of command delivery including timing intervals, batch sizes, and priority levels. By dynamically adjusting these parameters based on machine learning predictions and actual performance feedback, the system optimizes delivery speed while maintaining control device capacity within safe operating limits
3Reliability
If conventional priority-based data transmission is used, then high-priority data is transmitted first, but command delivery efficiency and load reduction are not improved
Solution Approach 1:
The machine learning device serves multiple functions simultaneously: it monitors control device performance, analyzes command patterns, predicts future command volumes, optimizes delivery schedules, and manages priority handling. This multi-functional approach integrates priority-based transmission with intelligent scheduling, achieving both reliable priority data transmission and improved overall command delivery efficiency
Data Source
AI summary
The machine learning device performs machine learning with respect to a computer device that issues a command for accessing a control device. The machine learning device has a state data acquisition unit that monitors a command issued by one or more applications of the computer device, and acquires state data including a command distribution schedule and reception and distribution times of a command distributed according to the distribution schedule, a behavior information output unit that outputs, to the computer device, behavior information including correction information for the distribution schedule in the state data, a reward calculation unit that calculates a reward with respect to the behavior information based on a distribution delay time of each command and an average distribution interval of all commands, and a value function update unit that updates a value function with respect to the state data and the behavior information based on the reward.