Adaptive Discount Factor for Reinforcement Learning
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Solution Overview
Problem
Reinforcement learning methods struggle to adapt to changing environments and efficiently detect both known and unknown patterns, especially on low-level computing devices with limited resources, as they rely on fixed discount factors and require significant computing power for advanced pattern detection.
Innovation Solution
A reinforcement learning method that automatically adjusts the discount factor based on the accuracy of evaluation models, switching between known and unknown pattern detection, and utilizing a gateway device to collect and analyze network packets, allowing for efficient threat detection on low-level devices by integrating both light and high-level evaluation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed discount factor is used in reinforcement learning, then the learning direction is stable at the start, but the system cannot adapt to changing environments and detect both known and unknown patterns effectively
Solution Approach 1:
The discount factor is transformed from a static fixed value to a dynamic variable that automatically adjusts during reinforcement learning based on the evaluation model's accuracy and environmental changes, enabling the system to adapt between exploring known patterns and detecting unknown patterns
Solution Approach 2:
The system implements feedback mechanisms where the evaluation model's results are used to adjust the discount factor, creating a closed-loop control system that continuously adapts the learning direction based on performance metrics and environmental responses
2Measurement precision
If advanced evaluation models are used to detect unknown patterns, then detection accuracy improves, but computational power requirements increase beyond what low-level devices can provide
Solution Approach 1:
The evaluation system is segmented into multiple levels: a lightweight evaluation model deployed on low-level devices for basic known pattern detection, and a more advanced evaluation model on servers for comprehensive unknown pattern detection, with results fed back to guide the reinforcement learning process
Solution Approach 2:
The discount factor adjustment mechanism acts as an intermediary that translates the outputs from evaluation models into appropriate learning directions, allowing low-level devices to leverage server-based model results without directly executing computationally intensive model inference
3Adaptability or versatility
If the discount factor is adjusted to focus on unknown pattern detection, then detection capability improves, but learning efficiency for known patterns decreases
Solution Approach 1:
The discount factor dynamically shifts between values optimized for known pattern detection and those optimized for unknown pattern detection based on real-time evaluation model performance, allowing the system to efficiently alternate between exploitation and exploration phases
Data Source
AI summary
There is provided a reinforcement learning method in which a discount factor is automatically adjusted, the method being executed by a computing device and comprising repeatedly training a reinforcement learning model, which determines an evaluation result of input data, using the input data, wherein the repeatedly training of the reinforcement learning model comprises obtaining first result data which is output as a result of inputting the input data to the reinforcement learning model. obtaining second result data which is the result of evaluating the input data using a first evaluation model. obtaining a first return which is the result of adding a discount factor to a first reward given in consideration of whether the first result data and the second result data match. training the reinforcement learning model using the first return and automatically adjusting the discount factor by considering the second result data.