Online Machine Learning Model Immediate Reward Allocation
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Solution Overview
Problem
Machine learning models face delays in receiving rewards, leading to stale learning and reduced accuracy in predicting subsequent actions due to the absence of timely feedback, which can result in prolonged training times and suboptimal performance.
Innovation Solution
A method that allocates an immediate reward as an approximation of the delayed reward to an online machine learning model when the real reward is not received within a specified time, allowing for controlled and dynamic retraining based on criteria such as meeting a dynamically defined threshold or being within upper and lower bounds of the expected reward.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the machine learning model waits for the real reward to be received before updating, then the model training accuracy is improved, but the training time is prolonged
Solution Approach 1:
The system performs preliminary actions by allocating immediate rewards to the machine learning model before the real delayed reward is received. This allows the model to begin updating with approximate feedback immediately, rather than waiting passively for the actual reward, thus reducing training time while maintaining accuracy through subsequent corrections when real rewards arrive.
Solution Approach 2:
The immediate reward serves as an intermediary element between the model's action and the delayed real reward. This intermediary provides timely feedback that bridges the temporal gap, allowing the model to learn from approximate signals while the system continues to monitor for the actual reward outcome, effectively mediating the timing conflict between immediate updates and accurate feedback.
2Measurement precision
If the machine learning model uses delayed real rewards for updates, then the model prediction accuracy is improved, but the model freshness is reduced
Solution Approach 1:
The system performs preliminary updates using immediate rewards before the delayed real reward arrives. This keeps the model fresh and continuously adapting to new data, while the subsequent arrival of real rewards allows for accuracy corrections, thus maintaining both model freshness and prediction accuracy over time.
Solution Approach 2:
The system ensures continuous useful action by allocating immediate rewards continuously as they become available, rather than pausing model updates until delayed rewards arrive. This continuous learning process maintains model freshness, while the incorporation of real rewards when they arrive ensures long-term prediction accuracy through iterative refinement.
3Productivity
If the machine learning model receives immediate rewards for training, then the training speed is improved, but the training reliability is reduced
Solution Approach 1:
The system implements a dual-feedback mechanism where immediate rewards provide continuous training signals for speed, while delayed real rewards provide ultimate validation for reliability. The model receives immediate feedback for rapid learning, then receives final feedback from real rewards to correct and verify its learning, thus achieving both speed and reliability through layered feedback.
Solution Approach 2:
The immediate reward acts as an intermediary training signal that accelerates learning without completely replacing the ultimate reliability check of real rewards. This intermediary enables fast preliminary training while the system maintains the option to verify and correct using actual delayed rewards, thus balancing training speed with reliability through the intermediary's temporary role.
Data Source
AI summary
An online machine learning model such as an autonomous agent predicts an action. A processor associated with, or running, the online machine learning model observes an environment for an interval of time for a real reward associated with the action. Responsive to determining that the real reward is not received within the interval of time, the processor determines based on a criterion whether to allocate an immediate reward received within the interval of time to the online machine learning model, where the immediate reward is an approximation of the real reward. Responsive to determining that the immediate reward is to be allocated, the processor allocates the immediate reward to the online machine learning model. The online machine learning model further learns or retrains itself based on the immediate reward.