RL Model Evaluation Using Divergence-Constrained Adversarial Noise
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Solution Overview
Problem
Reinforcement learning algorithms struggle with robustness when the actual state or environment deviates from the assumed one, often leading to a trade-off between robustness and performance, and existing methods add random noise without considering appropriate noise distributions.
Innovation Solution
An information processing apparatus and method that adds noise to a state based on a predetermined prior distribution, determining an adversarial noise distribution using a divergence constraint to evaluate and train models, ensuring robustness by generating appropriate noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random noise is added to a state to evaluate robustness, then the model can be tested for variations, but the noise may not be appropriate leading to excessive robustness or performance degradation
Solution Approach 1:
The patent changes the parameters of noise by determining an optimal noise distribution based on the action value function and divergence constraints, rather than using fixed random noise. This allows the noise characteristics to be dynamically adjusted to achieve the right balance between robustness and performance.
Solution Approach 2:
The patent introduces feedback mechanisms where the noise distribution is determined based on the action value function Q(s,a) and constrained by divergence measures. The system continuously adjusts the noise distribution based on how it affects the action values, creating a closed-loop optimization process.
2Reliability
If excessive robustness is prepared for noise that cannot occur in reality, then the model becomes more robust, but performance degrades in vain
Solution Approach 1:
The patent converts the harmful effect of arbitrary noise into a beneficial evaluation tool by using divergence constraints to measure how much the noise distribution deviates from the prior. This allows the system to intentionally introduce challenging noise while ensuring it remains within realistic bounds, turning potential performance degradation into a useful robustness evaluation mechanism.
3Measurement precision
If noise distribution is determined based on action value without constraints, then the noise can be optimized for evaluation, but the noise may deviate excessively from the assumed distribution
Solution Approach 1:
The patent applies preliminary anti-action by introducing divergence constraints before determining the final noise distribution. These constraints prevent the noise distribution from deviating too much from the prior distribution, counteracting the tendency of action value optimization to produce unrealistic noise patterns.
Data Source
AI summary
An information processing apparatus acquires noise according to a predetermined prior distribution, adds the noise to a state in an environment used in a model to be evaluated to calculate an action value of an action in a perturbed state; and determines a distribution of adversarial noise for the model to be evaluated. The apparatus determines the distribution of the adversarial noise based on the action value while adding a constraint using a divergence indicating closeness between the distribution of the adversarial noise and the predetermined prior distribution.