Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning

Source

arXiv

arxiv.org/abs/2607.12523v1

Read the source

Source

Plain-Language Brief

The paper introduces OOD-RL-Bench, a new framework for testing out-of-distribution (OOD) detection in reinforcement learning (RL). The framework is designed to evaluate the performance of detectors against various anomalies injected into RL trajectories. It was tested using a Deep Q-Network policy in the LunarLander-v3 environment and assessed using several metrics.

What the Document Says

  • The paper presents OOD-RL-Bench, a framework for evaluating out-of-distribution (OOD) detectors in reinforcement learning (RL).
  • The framework is designed to evaluate the performance of detectors against various anomalies injected into RL trajectories.
  • The authors integrated detectors and anomaly injectors through shared interfaces and configuration, allowing for easy evaluation of new scoring methods and perturbation families without modifying the core benchmark loop.
  • The framework was tested using a Deep Q-Network policy in the LunarLander-v3 environment.

Key Concepts Mentioned

  • reinforcement learning
  • out-of-distribution detection
  • OOD-RL-Bench
  • anomaly types
  • Deep Q-Network
  • LunarLander-v3

Claims Made by the Document

  • The current OOD detection benchmarks fail to account for the complex, action-dependent temporal structure inherent in RL trajectories.

Evidence Used

  • The text states that current OOD detection benchmarks evaluate image classifiers or static low-dimensional datasets, while the proposed framework is designed to handle the complex temporal structure of RL trajectories.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of out-of-distribution conditions is pivotal to determining when an agent's observations, transitions, or trajectory dynamics deviate from the assumptions underpinning its policy training. Current out-of-distribution (OOD) detection benchmarks typically evaluate image classifiers or static low-dimensional datasets, failing to account for the complex, action-dependent temporal structure inherent in RL trajectories. To address this gap, we present OOD-RL-Bench, a comprehensive and extensible framework designed to evaluate OOD detectors against categories of anomalies injected into RL trajectories. Detectors and anomaly injectors are integrated through shared interfaces and configuration, which allows new scoring methods and perturbation families to be evaluated without modification of the core benchmark loop. We evaluate the utility of the framework using a Deep Q-Network policy within the LunarLander-v3 environment. We assess the performance of each detector across a suite of anomaly types using matched-time AUROC, matched-time AUPRC, matched-time false-positive rate, detection delay, and segmented-onset metrics. Our analysis reveals significant performance variance across anomaly types: observation perturbations and regime switches are identified with high accuracy by several methods, while observation delay and action-conditioned dynamics remain difficult even when post-onset anomaly scores are compared against clean scores from the same timesteps. We make the framework, trained policy checkpoint, and complete results publicly available as a reproducible artefact.