Effect-Invariant Mechanisms for Policy Generalization.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning

Background:

• Policy learning is crucial for real-world systems but struggles with adaptation to new environments.
• Existing methods rely on full invariance of conditional distributions, which is often too restrictive.
• Generalizing policies to unseen scenarios remains a significant challenge in machine learning.

Purpose of the Study:

• To introduce a relaxed invariance condition, effect-invariance (e-invariance), for improved policy generalization.
• To demonstrate that e-invariance is sufficient for zero-shot policy generalization under specific assumptions.
• To extend the approach for few-shot policy generalization using limited test environment data.

Main Methods:

• Developed a novel concept of effect-invariance (e-invariance) as a relaxation of full invariance.
• Proved the sufficiency of e-invariance for zero-shot policy generalization.
• Designed data-driven testing procedures for e-invariance without assuming causal graphs or structural causal models.
• Investigated an extension for few-shot generalization with small test samples.

Main Results:

• Effect-invariance (e-invariance) is shown to be sufficient for zero-shot policy generalization.
• An extension enables effective few-shot policy generalization.
• Empirical validation using simulated and real-world mobile health intervention data confirms the approach's effectiveness.
• The method successfully tests e-invariance directly from data.

Conclusions:

• Effect-invariance offers a practical and powerful alternative to full invariance for policy learning.
• The proposed method enhances policy generalization capabilities in unseen environments.
• This work provides a robust framework for adapting policies efficiently with zero or few-shot learning.