Inverse Reinforcement Learning Feature Selection
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Solution Overview
Problem
In inverse reinforcement learning, designing features for the objective function is costly and challenging due to the need to consider various trade-off relationships, and existing methods struggle to derive ideal parameters effectively.
Innovation Solution
A learning device and method that includes a first inverse reinforcement learning unit to derive weights for candidate features, a feature selection unit to select features that estimate the closest reward to an ideal result using the Teaching Risk technique, and a second inverse reinforcement learning unit to generate a new objective function, thereby supporting the selection of features for inverse reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If features are manually designed for inverse reinforcement learning, then the objective function can capture complex trade-off relationships, but the design cost and time increase significantly
Solution Approach 1:
The system performs self-service by automatically selecting features through inverse reinforcement learning without requiring manual feature engineering. The algorithm autonomously identifies important features from the expert demonstration data, eliminating the need for expensive manual design while maintaining accurate reward estimation.
Solution Approach 2:
The patent replaces the mechanical process of manual feature design with an automated computational system. Instead of experts manually crafting features based on domain knowledge, the system uses inverse reinforcement learning algorithms to automatically derive features from demonstration data, substituting human cognitive work with computational processing.
2Adaptability or versatility
If all candidate features are used in inverse reinforcement learning, then the objective function becomes more comprehensive, but the complexity and computational cost increase
Solution Approach 1:
The system extracts only the most important features from the set of candidate features using inverse reinforcement learning. By identifying and selecting the subset of features that maximizes the objective function based on expert demonstrations, the system achieves comprehensive coverage without including all candidate features, thus reducing complexity.
Solution Approach 2:
Instead of using all candidate features (excessive action), the system applies partial action by selecting only the necessary subset of features that are most relevant to the task. This partial selection approach maintains the comprehensiveness needed for accurate reward estimation while avoiding the complexity of incorporating every possible feature.
3Extent of automation
If existing feature selection methods are used without ideal parameters, then automation is improved, but the selection accuracy deteriorates due to unclear ideal parameter derivation
Solution Approach 1:
The system uses feedback from expert demonstration data to iteratively improve feature selection. By comparing the learned policy with expert behavior and using the discrepancy as feedback, the inverse reinforcement learning algorithm refines its understanding of important features, achieving both automation and accuracy without requiring pre-specified ideal parameters.
Data Source
AI summary
A first inverse reinforcement learning execution unit 91 derives each weight of candidate features, which are plural features as candidates, included in a first objective function by inverse reinforcement learning using the candidate features. A feature selection unit 92 selects a feature when one feature is selected from the candidate features, from which each weight is derived, in such a manner that a reward represented using the feature is estimated to get the closest to an ideal reward result. A second inverse reinforcement learning execution unit 93 generates a second objective function by inverse reinforcement learning using the selected feature.