Criticality and Safety Margins for Reinforcement Learning

2024-09-26

arXiv.org

AI Summary

Existing reinforcement learning (RL) deployments lack an interpretable, quantifiable framework for identifying critical states—those whose occurrence significantly degrades agent performance. Method: This paper introduces a dual-scale criticality assessment: “true criticality,” defined as the expected reward degradation induced by policy deviation, and “proxy criticality,” a lightweight, monotonically estimable surrogate. We propose the novel concept of “safety margin”—the maximum number of steps within which confidence in safe execution remains above a specified tolerance—derived via policy perturbation analysis, statistical confidence interval estimation, Monte Carlo sampling, and monotonicity calibration. The approach is algorithm-agnostic (e.g., compatible with A3C) and requires no training modifications. Results: Evaluated on Atari Beamrider, monitoring only the 5% time steps with lowest safety margins captures 47% of failure events, enabling high-precision pre-failure warning and cost-effective human oversight.

Technology Category

Multiagent Systems: Adversarial AgentsMachine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Sequential Decision Making

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySecurity and Privacy: Large-scale security measurementsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

State of the art reinforcement learning methods sometimes encounter unsafe situations. Identifying when these situations occur is of interest both for post-hoc analysis and during deployment, where it might be advantageous to call out to a human overseer for help. Efforts to gauge the criticality of different points in time have been developed, but their accuracy is not well established due to a lack of ground truth, and they are not designed to be easily interpretable by end users. Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users. We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions. We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality. Safety margins make these interpretable, when defined as the number of random actions for which performance loss will not exceed some tolerance with high confidence. We demonstrate this approach in several environment-agent combinations; for an A3C agent in an Atari Beamrider environment, the lowest 5% of safety margins contain 47% of agent losses; i.e., supervising only 5% of decisions could potentially prevent roughly half of an agent's errors. This criticality framework measures the potential impacts of bad decisions, even before those decisions are made, allowing for more effective debugging and oversight of autonomous agents.

Problem

• Define criticality framework with quantifiable ground truth
• Develop interpretable safety margins for user significance
• Measure potential impacts of bad decisions preemptively

Innovation

• Defines true criticality via expected reward drop
• Introduces proxy criticality for low-overhead estimation
• Uses safety margins to interpret performance loss tolerance