aiqbench analysis
This research addresses a critical gap in safe reinforcement learning by shifting focus from mean episodic costs to the tail distribution of worst-case scenarios using Conditional Value-at-Risk (CVaR). The study demonstrates that policies appearing safe on average can still violate safety budgets in extreme episodes and investigates methods to control these tail violations without sacrificing overall return.
Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable?
arXiv:2610.09508v1 Announce Type: new Abstract: Safe reinforcement learning seeks policies that maximize return while satisfying constraints on cumulative cost. Most methods impose these constraints on expected episodic cost. Consequently, standard evaluations report mean episodic cost without characterizing how cost is distributed across episodes. A policy that satisfies the mean-cost criterion may therefore remain unsafe in its worst episodes. Mean-cost reporting neither identifies this tail violation nor shows whether it can be brought within budget while preserving return. In this work, we measure the episodic-cost tail using $\mathrm{CVaR}_{0.1}$, the average cost of the worst $10\%$ of episodes. We classify a policy as tail-safe when $\mathrm{CVaR}_{0.1}$ is within the safety budget. This allows us first to identify policies that are safe on average but unsafe in the tail and then to study whether their tail violations can be controlled while preserving return. To identify tail-unsafe policies, we evaluate five standard algorithms on three Safety-Gymnasium navigation tasks. We then examine four constraint families on dense-hazard navigation and assess tail control across four navigation and four locomotion tasks.
Title:Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable?
Abstract:Safe reinforcement learning seeks policies that maximize return while satisfying constraints on cumulative cost. Most methods impose these constraints on expected episodic cost. Consequently, standard evaluations report mean episodic cost without characterizing how cost is distributed across episodes. A policy that satisfies the mean-cost criterion may therefore remain unsafe in its worst episodes. Mean-cost reporting neither identifies this tail violation nor shows whether it can be brought within budget while preserving return. In this work, we measure the episodic-cost tail using $\mathrm{CVaR}_{0.1}$, the average cost of the worst $10\%$ of episodes. We classify a policy as tail-safe when $\mathrm{CVaR}_{0.1}$ is within the safety budget. This allows us first to identify policies that are safe on average but unsafe in the tail and then to study whether their tail violations can be controlled while preserving return. To identify tail-unsafe policies, we evaluate five standard algorithms on three Safety-Gymnasium navigation tasks. We then examine four constraint families on dense-hazard navigation and assess tail control across four navigation and four locomotion tasks.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.09508 [cs.LG]
(or arXiv:2610.09508v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.09508 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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