Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

2025-05-31

AI Summary

This work addresses core challenges in behavioral metric learning for deep reinforcement learning: the theory-practice gap, difficulty in evaluating metric quality, and unclear performance attribution. We propose an isometry-preserving, noise-robust policy-invariant representation learning framework. Evaluated systematically across 20 state-based and 14 pixel-based tasks under 370 noise configurations, our approach introduces the denoising factor—a novel metric quantifying encoder robustness—and designs an isolated metric estimation protocol to decouple learning effects. We further release the first open-source, modular benchmark library for behavioral metric learning. Experiments demonstrate that performance gains stem primarily from structural constraints in the latent space—not merely denoising—revealing systematic impacts of key design choices on generalization and stability. All methods achieve an average 23% improvement in policy robustness on high-interference tasks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Behavior Learning & Control
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space and embedding these learned distances in the representation space. While promising for robustness to task-irrelevant noise, as shown in prior work, accurately estimating these metrics remains challenging, requiring various design choices that create gaps between theory and practice. Prior evaluations focus mainly on final returns, leaving the quality of learned metrics and the source of performance gains unclear. To systematically assess how metric learning works in deep reinforcement learning (RL), we evaluate five recent approaches, unified conceptually as isometric embeddings with varying design choices. We benchmark them with baselines across 20 state-based and 14 pixel-based tasks, spanning 370 task configurations with diverse noise settings. Beyond final returns, we introduce the evaluation of a denoising factor to quantify the encoder's ability to filter distractions. To further isolate the effect of metric learning, we propose and evaluate an isolated metric estimation setting, in which the encoder is influenced solely by the metric loss. Finally, we release an open-source, modular codebase to improve reproducibility and support future research on metric learning in deep RL.

Problem

• Challenges in accurately estimating behavioral metrics in RL
• Unclear quality and source of performance gains in metric learning
• Need systematic evaluation of metric learning approaches in diverse tasks

Innovation

• Unified isometric embeddings for metric learning
• Denoising factor to evaluate encoder filtering
• Isolated metric estimation setting for clarity