Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction

Researchers propose behavior-aware auxiliary corrections for off-policy temporal-difference learning, introducing BA-TDC and BA-TDRC algorithms that replace standard covariance matrices with behavior Bellman matrices to improve stability in value-function approximation. The work provides theoretical convergence guarantees and demonstrates that behavior-aware geometry significantly benefits performance on certain tasks, though regularization remains necessary for robustness across diverse settings.

This paper addresses a fundamental challenge in reinforcement learning: stabilizing temporal-difference (TD) learning when training data comes from a different policy than the one being evaluated. The instability problem has long plagued off-policy TD methods, particularly when combined with function approximation, making this an active area of research since the foundational work on TDC and TDRC algorithms.

The authors' key innovation involves replacing the auxiliary covariance matrix with a behavior Bellman matrix that incorporates information about the data-generating policy. This behavior-aware approach separates two distinct improvements: the geometric contribution from policy-aware matrices versus the stabilizing contribution from regularization. By studying this in the linear prediction setting—a standard theoretical framework—the researchers create tractable analysis that extends insights to neural network approximation, where feature covariances and transition dynamics jointly influence learning dynamics.

The theoretical contributions include fixed-point preservation proofs and almost-sure convergence guarantees under Hurwitz stability conditions, providing rigorous backing for the proposed methods. The experiments reveal nuanced findings: behavior-aware geometry alone provides substantial benefits on some tasks but insufficient robustness on harder problems, explaining why regularization remains essential in practical applications.

For the broader AI/ML community, this work refines our understanding of how off-policy learning algorithms interact with function approximation. While not immediately impacting deployed systems, the insights about behavior-aware geometry design principles could influence future reinforcement learning frameworks, particularly in robotics and control applications where sample efficiency and stability are critical.

• →Behavior-aware auxiliary matrices provide more stable off-policy TD learning compared to standard covariance corrections in specific scenarios.
• →Regularization remains necessary for robust performance across diverse task difficulties despite behavior-aware geometric improvements.
• →Linear theoretical analysis successfully predicts auxiliary geometry effects in neural network value approximation last-layer dynamics.
• →The two-step BA-TDC and BA-TDRC construction isolates behavior-aware contributions from regularization effects for clearer understanding.
• →Convergence guarantees depend on Hurwitz stability conditions of the mean system, providing actionable theoretical criteria for algorithm design.