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Safety Margins for Reinforcement Learning

Alexander Grushin, Walt Woods, Alvaro Velasquez, Simon Khan

TL;DR

The work tackles safe deployment of autonomous controllers by defining true criticality as the expected reward loss under random action sequences and introducing real-time proxy metrics to estimate this risk. It presents safety margins $s(c_{proxy}(\ ), \zeta;\pi)$ that bound how many random actions can be tolerated before the likelihood of exceeding a discount-reward threshold is too high, enabling practical online monitoring. The method is calibrated via horizon-based approximations and density analyses to relate proxies to true criticality, yielding lookup-tables usable for real-time human oversight. Experiments with BeamRider using APE-X and A3C show margins decrease near failures and that proxy signals can be informative, highlighting both practical utility and avenues for improving proxy metrics.

Abstract

Any autonomous controller will be unsafe in some situations. The ability to quantitatively identify when these unsafe situations are about to occur is crucial for drawing timely human oversight in, e.g., freight transportation applications. In this work, we demonstrate that the true criticality of an agent's situation can be robustly defined as the mean reduction in reward given some number of random actions. Proxy criticality metrics that are computable in real-time (i.e., without actually simulating the effects of random actions) can be compared to the true criticality, and we show how to leverage these proxy metrics to generate safety margins, which directly tie the consequences of potentially incorrect actions to an anticipated loss in overall performance. We evaluate our approach on learned policies from APE-X and A3C within an Atari environment, and demonstrate how safety margins decrease as agents approach failure states. The integration of safety margins into programs for monitoring deployed agents allows for the real-time identification of potentially catastrophic situations.

Safety Margins for Reinforcement Learning

TL;DR

The work tackles safe deployment of autonomous controllers by defining true criticality as the expected reward loss under random action sequences and introducing real-time proxy metrics to estimate this risk. It presents safety margins that bound how many random actions can be tolerated before the likelihood of exceeding a discount-reward threshold is too high, enabling practical online monitoring. The method is calibrated via horizon-based approximations and density analyses to relate proxies to true criticality, yielding lookup-tables usable for real-time human oversight. Experiments with BeamRider using APE-X and A3C show margins decrease near failures and that proxy signals can be informative, highlighting both practical utility and avenues for improving proxy metrics.

Abstract

Any autonomous controller will be unsafe in some situations. The ability to quantitatively identify when these unsafe situations are about to occur is crucial for drawing timely human oversight in, e.g., freight transportation applications. In this work, we demonstrate that the true criticality of an agent's situation can be robustly defined as the mean reduction in reward given some number of random actions. Proxy criticality metrics that are computable in real-time (i.e., without actually simulating the effects of random actions) can be compared to the true criticality, and we show how to leverage these proxy metrics to generate safety margins, which directly tie the consequences of potentially incorrect actions to an anticipated loss in overall performance. We evaluate our approach on learned policies from APE-X and A3C within an Atari environment, and demonstrate how safety margins decrease as agents approach failure states. The integration of safety margins into programs for monitoring deployed agents allows for the real-time identification of potentially catastrophic situations.
Paper Structure (3 sections, 2 figures, 1 table)

This paper contains 3 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Results
  3. Methods

Figures (2)

  • Figure 1: Lookup tables (drawn as heatmaps) displaying safety margins in the proxy criticality metric vs. tolerance space (loss in reward), for APE-X (left) and A3C (right).
  • Figure 2: Normalized (over the Y-axis) kernel density plots for A3C capturing the relationship between true criticality and proxy criticality, with contours and trendlines, for 1 (left) vs. 16 (right) random actions.