Information processing apparatus, information processing method, method for evaluating machine learning model, learning method of machine learning model, and storage medium
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Solution Overview
Problem
Reinforcement learning algorithms struggle with robustness when the actual environment deviates from the assumed environment, leading to a trade-off between ensuring robustness and performance, and existing methods for adding noise do not effectively address this issue.
Innovation Solution
An information processing apparatus that calculates an adversarial distribution of an environment using a divergence constraint to minimize the action value of a model, ensuring robustness by adding noise that follows a predetermined prior distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to change state for robustness evaluation, then robustness is improved, but performance during normal operation degrades
Solution Approach 1:
The patent changes the parameter of noise distribution from uniform random noise to adversarial distribution that minimizes action value while constrained by prior distribution. This parameter change allows the system to evaluate robustness under worst-case scenarios without completely sacrificing normal operation performance, as the adversarial distribution is constrained to remain close to the prior distribution.
Solution Approach 2:
The patent applies partial action by adding noise only during evaluation phases rather than continuously during operation. The noise addition is selectively applied when robustness evaluation is needed, allowing the model to maintain high performance during normal operation while still achieving robustness evaluation under perturbed conditions.
2Reliability
If excessive robustness is prepared for rare noise events, then robustness is improved, but performance degrades in vain
Solution Approach 1:
The patent converts the harmful effect of rare noise events into a beneficial evaluation mechanism. By using adversarial distribution that minimizes action value, the system identifies the worst-case scenarios and uses them for targeted robustness evaluation, rather than preparing for all possible rare events indiscriminately. This allows the system to focus computational resources on the most critical robustness challenges.
Solution Approach 2:
The patent implements feedback through the divergence constraint that continuously monitors the distance between the adversarial distribution and the prior distribution. This feedback mechanism ensures that the adversarial distribution remains within acceptable bounds, preventing over-preparation for rare events that would unnecessarily degrade performance. The divergence constraint provides real-time guidance to balance robustness evaluation with performance maintenance.
3Adaptability or versatility
If random noise is added to evaluate robustness, then evaluation coverage is improved, but evaluation precision deteriorates
Solution Approach 1:
The patent inverts the conventional approach by instead of randomly sampling from the prior distribution, it constructs the adversarial distribution that specifically minimizes the action value function. This inversion allows the system to focus evaluation precision on the most critical failure modes rather than uniformly covering all possible scenarios, thereby improving measurement precision while maintaining evaluation coverage through the divergence constraint.
Data Source
AI summary
An information processing apparatus acquires a predetermined prior distribution of an environmental parameter, calculates an action value of an action in each of environments perturbed by the environmental parameter; and determines an adversarial distribution of an environment for a model to be processed based on the action value. The apparatus determines the adversarial distribution of the environment that reduces the action value of the model to be processed while adding a constraint using a divergence indicating closeness between the adversarial distribution of the environment and the predetermined prior distribution.