Discovering Higher-Level Actions from Expert Demonstrations
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Solution Overview
Problem
Current Reinforcement Learning (RL) and planning systems face difficulties in discovering temporally-extended high-level actions due to complex states and action spaces, making it challenging to ease planning and RL processes.
Innovation Solution
A computer-implemented method is developed to detect higher-level actions by training multiple predictors with different durations to predict future states from experts' action demonstrations, determining matches between predicted and actual future states, and outputting pairs of matching states as higher-level actions, using an ensemble of multilayer-perceptrons with an hourglass structure and a loss function to identify bottlenecks for subtask discovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If temporally-extended high-level actions are discovered to ease planning and Reinforcement Learning, then productivity and ease of operation are improved, but device complexity and difficulty of detecting and measuring increase due to complicated states and action spaces
Solution Approach 1:
The patent segments the complex state space by training multiple predictors with different durations to detect higher-level actions at different temporal scales. This divides the complicated planning problem into manageable segments, where each predictor handles specific time durations, making the overall system more tractable despite the inherent complexity of the state space.
2Measurement precision
If multiple predictors with different durations are trained to predict future states, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing predictors that can handle multiple time durations through a unified architecture. Each predictor in the ensemble serves multiple functions by predicting future states at different temporal horizons, reducing the need for separate specialized predictors for each duration and thereby managing complexity while maintaining precision.
3Measurement precision
If matches between predicted and actual future states are determined to identify higher-level actions, then manufacturing precision and measurement precision are improved, but loss of time and computational resources increase
Solution Approach 1:
The patent implements partial action by determining matches between predicted and actual future states only at strategically selected time points and durations. Rather than exhaustively comparing all possible state transitions, the system performs partial matching operations that are sufficient to identify higher-level actions, thereby reducing computational time and resource consumption while maintaining detection accuracy.
Data Source
AI summary
A method is provided for detecting a higher-level action from one or more trajectories of real states. The trajectories are based on an experts' action demonstration. The method trains predictors to predict future states. Each predictor has a different duration of the higher-level action to be detected. The method predicts, using the predictors, the future states using past ones of the real states in the one or more trajectories as inputs for the predictors. The method determines if a match exists between any of the future states relative to a real future state with a corresponding same duration from the one or more trajectories. The method outputs a pair that includes the matching one of the future states as a prediction input and the real future state with the corresponding same duration from the one or more trajectories as the higher-level action corresponding thereto, responsive to the match existing.