Near-Constant Strong Violation and Last-Iterate Convergence for Online CMDPs via Decaying Safety Margins

2026-02-11 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

Qian Zuo, Zhiyong Wang, and Fengxiang He introduce FlexDOME, a novel algorithm designed for safe online reinforcement learning within Constrained Markov Decision Processes (CMDPs). This algorithm addresses limitations in existing primal-dual methods, which often suffer from growing strong constraint violation or are restricted to average-iterate convergence due to inherent oscillations. FlexDOME is the first to provably achieve near-constant \(\tilde{O}(1)\) strong constraint violation, alongside sublinear strong regret and non-asymptotic last-iterate convergence. It integrates time-varying safety margins and regularization terms into the primal-dual framework. The theoretical underpinnings involve a term-wise asymptotic dominance strategy, rigorously scheduling the safety margin to majorize functional decay rates of optimization and statistical errors, thereby clamping cumulative violations. Experimental results validate these theoretical findings.

Key takeaway

For research scientists developing safe online reinforcement learning algorithms, FlexDOME offers a significant advancement by achieving near-constant strong constraint violation and last-iterate convergence. You should consider integrating its time-varying safety margin and regularization techniques into your primal-dual frameworks to overcome the limitations of growing violations and average-iterate convergence in CMDPs. This approach provides a robust solution for maintaining safety guarantees in dynamic environments.

Key insights

FlexDOME achieves near-constant strong constraint violation and last-iterate convergence in online CMDPs.

Principles

• Strong regret forbids error cancellation.
• Time-varying safety margins improve stability.

Method

FlexDOME incorporates time-varying safety margins and regularization terms into a primal-dual framework, using a term-wise asymptotic dominance strategy to schedule safety margins and a policy-dual Lyapunov argument for convergence.

In practice

• Apply FlexDOME for safe online RL.
• Use time-varying safety margins in CMDPs.

Topics

• Online Reinforcement Learning
• Constrained MDPs
• Strong Constraint Violation
• Last-Iterate Convergence
• FlexDOME Algorithm

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.