Hierarchical Mixtures of Experts Learning Accuracy
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Solution Overview
Problem
Current methods for learning hierarchical mixtures of experts using inverse reinforcement learning lack sufficient accuracy, particularly when combined with model-free approaches and importance sampling, which can lead to suboptimal estimation of reward functions.
Innovation Solution
A learning device and method that incorporates an EM algorithm for initial learning, switching to factorized asymptotic Bayesian inference when predetermined conditions are met, to enhance the estimation accuracy of hierarchical mixtures of experts by inverse reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inverse reinforcement learning is combined with hierarchical mixtures of experts using traditional methods, then the model can handle complex intentions, but the estimation accuracy is insufficient
Solution Approach 1:
The patent segments the learning process into two distinct phases: EM algorithm for initial parameter estimation and factorized asymptotic Bayesian inference for refinement. This segmentation allows each method to focus on specific aspects of the learning task, improving overall estimation accuracy while managing complexity through structured progression
Solution Approach 2:
The patent applies preliminary action by using the EM algorithm to obtain initial parameter estimates before applying factorized asymptotic Bayesian inference. This preliminary estimation provides a solid foundation for the subsequent refinement process, ensuring that the complex Bayesian inference starts from a reasonable initial state rather than random parameters
2Adaptability or versatility
If multiple intentions are modeled in the reward function, then the model can represent complex expert behavior, but the reward function becomes difficult to interpret
Solution Approach 1:
The hierarchical mixtures of experts structure segments the reward function into multiple expert components, each representing a simple intention. The gate function further segments the selection process, choosing which expert to apply based on the current state. This segmentation maintains interpretability by keeping individual experts simple while achieving versatility through their combination
Solution Approach 2:
Different parts of the reward function (different experts) have different local qualities or characteristics. Each expert is designed to handle specific types of situations with simple, interpretable reward structures. The gate function dynamically selects which local quality to apply based on the current state, maintaining both versatility and interpretability
3Adaptability or versatility
If model-free inverse reinforcement learning with importance sampling is used, then the method can be applied broadly, but the estimation accuracy deteriorates
Solution Approach 1:
The patent changes the parameters and assumptions of the learning method by transitioning from pure model-free importance sampling to factorized asymptotic Bayesian inference. This parameter change involves incorporating asymptotic assumptions and factorized probability structures that significantly improve estimation accuracy while maintaining the broad applicability of the inverse reinforcement learning framework
Data Source
AI summary
An input unit 81 receives input of a decision-making history of a subject. A learning unit 82 learns hierarchical mixtures of experts by inverse reinforcement learning based on the decision-making history. An output unit 83 outputs the learned hierarchical mixtures of experts. The learning unit 82 learns the hierarchical mixtures of experts using an EM algorithm, and when a learning result using the EM algorithm satisfies a predetermined condition, learns the hierarchical mixtures of experts by factorized asymptotic Bayesian inference.