Inverse Reinforcement Learning Objective Function Adaptation
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Solution Overview
Problem
Existing methods for learning the objective function using inverse reinforcement learning may deviate from the true objective function due to changes in trends, social issues, and clientele, making it difficult to reflect user intentions, especially when infrequent decision-making data is scarce.
Innovation Solution
A learning device and method that outputs multiple optimization results for a target using pre-generated objective functions, accepts user instructions to select and modify these results, and updates the objective function using decision-making history data to align with user intentions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inverse reinforcement learning is used to learn the objective function from decision making history data, then the objective function can be estimated to reproduce skilled person's decision making, but the learned objective function may deviate from the true objective function when trends, social issues, or clientele change over time
Solution Approach 1:
The system dynamically updates the objective function by integrating new decision making history data over time. The learning unit continuously relearns the objective function using updated data, allowing the system to adapt to changing trends, social issues, and clientele while maintaining accuracy through iterative refinement of the reward function parameters.
2Adaptability or versatility
If the objective function is relearned using decision making history data, then the objective function can reflect current user intentions, but it is difficult to collect sufficient data when decisions occur infrequently
Solution Approach 1:
The system performs preliminary learning using available decision making history data to establish an initial objective function before sufficient data accumulates. This preliminary objective function enables the system to operate and begin capturing new decision making patterns, creating a bootstrap effect where early learning facilitates future data collection and refinement.
Solution Approach 2:
The system implements continuous feedback loops where the learned objective function is applied to generate recommendations, user selections are captured as new decision making history data, and this feedback data is used to iteratively improve the objective function. This feedback mechanism allows the system to progressively refine its understanding even with initially limited data.
3Adaptability or versatility
If multiple objective functions are generated by inverse reinforcement learning, then the system can provide multiple optimization results for user selection, but the complexity of the learning system increases
Solution Approach 1:
The system segments the learning process into distinct functional units: an inverse reinforcement learning unit that generates multiple candidate objective functions, a selection acceptance unit that presents these as multiple optimization results to the user, and a learning unit that integrates user selections. This segmentation allows multiple objective functions to be managed through modular, independent components rather than a monolithic complex system.
Data Source
AI summary
The target output means 91 outputs a plurality of second targets, which are optimization results for a first target using one or more objective functions generated in advance by inverse reinforcement learning based on decision making history data indicating an actual change to a target. The selection acceptance means 92 accepts a selection instruction from a user for a plurality of the output second targets. The data output means 93 outputs the actual change from the first target to the accepted second target as the decision making history data. The learning means 94 learns the objective function using the decision making history data.