Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning
This paper proposes using adjustment speed as a safety constraint for nonstationary reinforcement learning. The key idea is to define safety in terms of adaptation feasibility: when the required adaptation exceeds the system's calibrated recovery capacity, the framework proactively tightens actions and activates shielding to reduce unsafe behavior. Experiments in a driving environment show reduced safety violations during short windows aligned with context changes.
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[Submitted on 21 Jul 2026]
Title:Adjustment Speed as a Safety Constraint for Nonstationary Reinforcement Learning
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Abstract:Ensuring safety in reinforcement learning under nonstationarity requires determining whether a learning system can safely adapt to forecasted environmental change within the required recovery horizon. Existing safe reinforcement learning methods typically assume stationary environments and do not explicitly consider adaptation speed as a safety concern. However, when environments evolve over time, delayed adaptation may result in transient unsafe behavior.
This paper proposes adjustment speed as a safety constraint for nonstationary reinforcement learning. The central idea is to define safety in terms of adaptation feasibility: future states or regions may become unsafe when the adaptation required to remain safe exceeds the learning system's calibrated recovery capacity. The proposed framework uses learned context representations and short-horizon context forecasts to estimate adaptation demand and compare it with the agent's achievable adaptation capacity.
When predicted adaptation demand exceeds the calibrated recovery capacity, the framework proactively tightens the admissible action set and activates an action-level shield to reduce unsafe behavior before violations occur.
Experiments in a nonstationary driving environment show that the proposed approach primarily reduces safety violations in short-horizon windows aligned with context changes. Ablation studies further show that shielding is more conservative for peak- and tail-risk suppression, while optimization-level adjustment provides additional reductions in short-horizon switch-conditioned violations.
These results support adaptation feasibility as a practical safety principle for reinforcement learning under nonstationarity and demonstrate that proactive intervention can improve safety during periods of environmental change.
Comments: 15 pages, 5 figures, 2 tables. Preprint
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.21646 [cs.LG]
(or arXiv:2607.21646v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.21646
arXiv-issued DOI via DataCite
Submission history
From: Timofey Tomashevskiy [view email] [v1] Tue, 21 Jul 2026 23:52:07 UTC (405 KB)
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