Episodic Off-Policy Evaluation for Joint Actions Across User Episodes
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Solution Overview
Problem
Current off-policy evaluation methods for online systems fail to account for the joint effects of multiple episodes when evaluating machine-learned policies, leading to inaccurate predictions of user behavior and potential losses in expected profits.
Innovation Solution
An online concierge system performs episodic off-policy evaluations by dividing time intervals into regular periods and applying a multi-episode framework, using inverse propensity scores to evaluate the performance of new policies across multiple user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-episode off-policy evaluation is used, then evaluation simplicity is maintained, but evaluation accuracy deteriorates due to ignoring joint effects of multiple episodes
Solution Approach 1:
The patent segments the evaluation process into episode-level components while maintaining sequence awareness. It divides the user interaction history into discrete episodes and applies segmentation to the importance weight calculation, computing weights for each episode while preserving the temporal sequence. This allows accurate joint effect evaluation without overwhelming complexity by breaking down the multi-episode evaluation into manageable episode-level units that can be processed systematically.
2Reliability
If multi-episode framework is applied, then user behavior prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing importance weights for each episode based on the logging policy before performing the multi-episode evaluation. These pre-computed weights are then reused in the final value estimation, avoiding redundant calculations. The system prepares episode-level statistics and propensity scores in advance, which significantly reduces the computational burden when evaluating the joint effects of multiple episodes while maintaining high prediction accuracy.
3Loss of information
If joint effects of multiple episodes are accounted for, then policy evaluation completeness is improved, but data processing complexity worsens
Solution Approach 1:
The patent introduces a temporal dimension to the evaluation by considering the sequence of episodes rather than treating them as independent single-episode events. It transforms the evaluation from a static single-episode analysis to a dynamic multi-episode sequence analysis, adding the time/sequence dimension to capture joint effects. This dimensional expansion allows complete information capture about user behavior patterns across episodes while managing complexity through structured temporal processing.
Data Source
AI summary
An off-policy evaluation system performs episodic off-policy evaluations to perform off-policy evaluation (OPE) for multiple, joint episodes. For a single episode, a first machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. For a second episode, a second machine learning model outputs a propensity for each action for the user and selects a first action for the user from the set of propensities. The second machine learning model is evaluated by determining an importance weight for the first model and the second model to determine the inverse propensity score of the second machine learning model.