RL Action Explainer Using Occupation Measures for Decision Transparency
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Solution Overview
Problem
Reinforcement learning models often operate as black boxes, making it difficult for users to understand the features driving their decisions, especially in critical domains like healthcare and transportation, where explainability is crucial.
Innovation Solution
An automated explainer system identifies features driving reinforcement learning model actions using occupation measures of state-action pairs, applying techniques like PCA for dimensionality reduction to provide clear explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning models are used to maximize cumulative reward in complex domains, then decision-making performance is improved, but explainability of decisions deteriorates
Solution Approach 1:
The patent introduces occupation measures as an intermediary representation that bridges the black-box reinforcement learning model and human interpreters. By computing occupation measures from state-action pairs and applying dimensionality reduction, the system creates an intermediate layer that preserves decision information in a human-understandable format without altering the underlying RL model's performance.
Solution Approach 2:
The patent extracts explanatory features from the internal state-action representations of the reinforcement learning model by computing occupation measures. This extraction process isolates the critical decision-driving features from the complex model internals, presenting them separately for human interpretation while leaving the original model intact and operational.
2Loss of information
If features are extracted from state-action pairs to improve explainability, then user understanding is improved, but computational complexity increases
Solution Approach 1:
The patent transforms the high-dimensional state-action pair representations into reduced-dimensional occupation measure representations by changing the parameter space. This parameter transformation maintains the essential decision information while reducing dimensionality, thereby improving explainability without requiring excessively complex computational resources.
Solution Approach 2:
The patent applies dimensionality reduction techniques to project state-action pairs from high-dimensional state space into a lower-dimensional occupation measure space. This dimensional transformation preserves the critical information needed for explanation while reducing computational complexity and making the results more interpretable for users.
Data Source
AI summary
Automatic identification of features that drive a reinforcement learning model to recommend an action of interest. The identification is based on a calculation of occupation measures of state-action pairs associated with the reinforcement learning model. High occupation measures of certain action-state pairs indicate that the states of these pairs likely include the sought-after features.