Reinforcement Learning Noise Distribution for Robust Policy Training
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Solution Overview
Problem
Reinforcement learning algorithms struggle with robustness when the actual environment deviates from the assumed state, often leading to a trade-off between ensuring robustness and performance, and existing methods add random noise that can degrade performance unnecessarily.
Innovation Solution
An information processing apparatus determines a distribution of adversarial noise using a predetermined prior distribution to train action and policy functions, constraining the noise distribution to minimize action values while maintaining closeness to the prior distribution, thereby ensuring robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random noise is added to the state to evaluate robustness, then the model can consider variations of the state, but the performance of the controller during normal operation degrades
Solution Approach 1:
The patent changes the parameter of noise distribution from uniform random noise to adversarial noise following a specific distribution (e.g., normal distribution with mean 0 and standard deviation σ). This allows the noise to be structured and controlled, enabling robustness evaluation while maintaining acceptable performance during normal operation by adjusting the noise intensity parameter σ.
2Reliability
If excessive robustness is prepared for noise that cannot occur in reality, then the model becomes overly conservative, but the performance in real-world applications deteriorates
Solution Approach 1:
The patent introduces a noise intensity parameter σ that can be adjusted to control the degree of robustness. By changing this parameter, the system can adapt the noise level to match real-world conditions, preventing over-conservatism while ensuring sufficient robustness for practical applications.
Solution Approach 2:
The patent enables dynamic adjustment of the noise distribution parameters based on operational context. The system can switch between different noise intensity levels or distributions depending on whether the model is being trained for robustness or deployed for performance-critical operations, making the robustness preparation flexible and adaptive.
3Reliability
If the noise distribution is constrained to be close to the prior distribution, then the action value is reduced under constraint, but the ability to evaluate extreme robustness scenarios is limited
Solution Approach 1:
The patent uses a divergence-based constraint (such as KL divergence) to control the noise distribution proximity to the prior distribution. By adjusting the constraint parameter, the system can balance between maintaining action value reduction and allowing sufficient deviation to evaluate extreme robustness scenarios. The constraint parameter acts as a knob to tune the trade-off between these two objectives.
Data Source
AI summary
An information processing apparatus determines a distribution of adversarial noise for a model to be processed using a predetermined prior distribution; and trains at least one of an action value function or a policy function of the model to be processed based on an action value of an action in a perturbed state obtained by adding the adversarial noise to a state in an environment used in the model to be processed. The apparatus determines the distribution of the adversarial noise that reduces the action value of the model to be processed under a constraint using a divergence indicating closeness between the distribution of the adversarial noise and the predetermined prior distribution.