Clustered Reinforcement Learning for Selective Forecast Revision
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Solution Overview
Problem
Existing machine learning systems, particularly in automated forecasting, suffer from inefficiencies such as revising all forecasts regardless of accuracy, lack of context utilization, and producing inaccurate results, necessitating a more focused and accurate approach.
Innovation Solution
A hierarchical, clustered reinforcement learning system that clusters objects based on characteristics, selectively revises clusters, and performs reinforcement learning on individual objects within selected clusters, combining machine and human expertise for optimized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all automated forecasts are revised, then forecast accuracy may be improved, but processing time and system resources are wasted on forecasts that do not need revision
Solution Approach 1:
The patent segments the forecast revision process by clustering forecasts into groups based on characteristics such as product category, seasonality, and historical accuracy. This allows the system to apply different revision strategies to different clusters, revising only those clusters that need improvement rather than all forecasts uniformly, thereby reducing processing time while maintaining accuracy for critical forecasts.
Solution Approach 2:
The patent applies local quality by identifying specific clusters or individual forecasts that exhibit poor accuracy characteristics and directing revision resources specifically to those areas. The system uses quality metrics to determine which clusters require revision, concentrating expert knowledge and processing power on the most problematic forecasts rather than applying uniform revision across all forecasts.
2Measurement precision
If reinforcement learning is performed on all subject objects, then learning accuracy may be improved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent segments the set of subject objects into multiple clusters based on shared characteristics. Reinforcement learning is then performed at the cluster level rather than on each individual object, significantly reducing the computational burden. The system trains cluster-level agents that can generalize across multiple objects, maintaining learning accuracy while improving processing efficiency.
Solution Approach 2:
The patent merges multiple similar subject objects into clusters and applies reinforcement learning to the cluster as a whole. By combining objects with similar characteristics into single learning units, the system reduces the number of independent learning processes required, thereby conserving computational resources and time while still achieving effective learning outcomes.
3Measurement precision
If expert knowledge is applied to all forecasts, then forecast quality improves, but the system becomes less efficient and more costly
Solution Approach 1:
The patent applies expert knowledge locally to specific clusters or individual forecasts that demonstrate poor performance or high variability. The system uses quality metrics to identify which forecasts benefit most from expert review, directing human expertise only to those cases rather than applying it uniformly across all forecasts, thereby improving efficiency while maintaining quality where needed.
Solution Approach 2:
The patent implements a self-service mechanism where the system automatically identifies and revises forecasts that meet certain quality criteria through automated algorithms. This reduces the need for manual expert review by handling routine forecast adjustments automatically, reserving expert knowledge for more complex or problematic cases that truly require human judgment.
Data Source
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AI summary
A system and method for hierarchical, clustered reinforcement learning is disclosed. A plurality of subject objects may be obtained, and a plurality of clusters of the subject objects may be determined. Clustered reinforcement learning may be performed on each cluster, including training a respective cluster agent for the each cluster. A first cluster of the plurality of clusters may be selected for revision based on selection criteria. After selection of the selected first cluster, individual reinforcement learning may be performed on each individual subject object included in the selected first cluster, including training a respective individual agent for the each individual subject object. An action may be controlled based on a result of the hierarchical, clustered reinforcement learning.