Active Imitation Learning via Disagreement-Based Expert Querying
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Imitation learning in complex, dynamic environments requires extensive expert training data, especially in high-dimensional and continuous decision spaces, making it costly and inefficient.
Innovation Solution
An active learning engine that queries expert trajectories only for critical decision points, using disagreement functions to identify areas of high uncertainty, reducing the need for extensive expert interaction and optimizing policy generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If imitation learning uses extensive expert trajectories for training, then the accuracy and reliability of the learned policy is improved, but the cost and time required for data collection increases significantly
Solution Approach 1:
The patent applies partial action by querying expert trajectories selectively rather than exhaustively. The active learning engine identifies and queries only the most informative states where the policy is most uncertain, rather than querying all possible states. This partial querying approach maintains policy accuracy while dramatically reducing the time and cost of data collection.
Solution Approach 2:
The system employs self-service through the active learning engine that autonomously identifies which expert queries are most valuable. The disagreement function automatically detects states where multiple policies conflict, and the engine selectively queries experts only for those critical states, making the training process self-optimizing without requiring manual intervention for every query decision.
2Reliability
If imitation learning queries expert trajectories for all iterations, then the policy convergence is improved, but the computational cost and expert interaction requirements increase
Solution Approach 1:
The patent implements partial action by querying expert trajectories only for a subset of iterations rather than all iterations. The active learning engine monitors policy convergence and selectively queries experts when disagreement is detected, stopping queries once sufficient convergence is achieved. This maintains reliable policy convergence while improving training efficiency by reducing unnecessary expert interactions.
Solution Approach 2:
The system uses feedback mechanisms where the disagreement function continuously monitors policy performance and feeds this information back to the active learning engine. When disagreement exceeds thresholds, the engine queries experts for additional training data; when disagreement is low, querying stops. This feedback loop ensures reliable convergence while optimizing computational efficiency.
3Adaptability or versatility
If the decision space has high dimensionality and continuous variables, then the applicability to complex real-world problems is improved, but the amount of training data required increases exponentially
Solution Approach 1:
The active learning engine provides self-service by automatically identifying which regions of the high-dimensional state space require expert queries. The disagreement function traverses the decision space and autonomously selects states with highest uncertainty, eliminating the need for exhaustive sampling of the entire high-dimensional space. This maintains applicability to complex environments while reducing training data requirements.
Solution Approach 2:
The patent applies local quality by focusing expert queries on specific local regions of the state space where disagreement occurs, rather than uniformly sampling the entire high-dimensional space. The active learning engine identifies critical local areas needing refinement and queries experts only for those regions, maintaining policy quality in complex environments while minimizing overall data requirements.
4Measurement precision
If expert trajectories are queried frequently during training, then the policy accuracy is improved, but the financial cost of expert interaction increases
Solution Approach 1:
The patent applies partial action by querying experts only when necessary rather than frequently at every training step. The active learning engine uses the disagreement function to identify specific states where policy precision needs improvement, and queries experts only for those critical cases. This maintains high policy precision while dramatically improving cost efficiency by eliminating unnecessary expert interactions.
Solution Approach 2:
The system implements feedback where the disagreement function continuously monitors policy precision and provides feedback to the active learning engine. When disagreement indicates insufficient precision, the engine queries experts for additional training data; when precision is sufficient, querying stops. This feedback mechanism ensures high policy precision while optimizing cost efficiency by querying experts only when truly necessary.
Data Source
AI summary
According to one embodiment, a computer-implemented method for active, imitation learning, includes: providing training data comprising an expert trajectory to a processor; querying the expert trajectory during an iterative, active learning process; generating a decision policy based at least in part on the expert trajectory and a result of querying the expert trajectory; attempting to distinguish the decision policy from the expert trajectory; in response to distinguishing the decision policy from the expert trajectory, outputting a policy update and generating a new decision policy based at least in part on the policy update; and in response to not distinguishing the decision policy from the expert trajectory, outputting the decision policy. Importantly, the expert trajectory is queried for only a subset of iterations of the iterative, active learning process, wherein the most uncertain state/action pair(s) from the expert trajectory are determined using one or more disagreement functions.