arXiv:2601. 22993v4 Announce Type: replace Abstract: We introduce Canary, a risk-averse method designed to optimize Value-at-Risk (VaR) constrained reinforcement learning (RL) problems.
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The paper introduces Canary, a risk‑averse reinforcement learning method that optimizes Value‑at‑Risk (VaR) constraints. By applying Cantelli’s inequality, Canary derives a tractable, conservative, and smooth bound on the VaR constraint using only the first two moments of the cost return, yielding a stable constraint estimator even with tight violation thresholds. Extending the trust‑region framework of Constrained Policy Optimization (CPO), the authors provide worst‑case bounds for policy improvement and constraint violation, and empirically demonstrate that Canary reliably satisfies the VaR constraint in every tested environment.
arXiv:2606. 14029v1 Announce Type: new Abstract: Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints.
arXiv:2607. 15457v1 Announce Type: new Abstract: We study robust peak-cost constrained reinforcement learning (RP-CRL), where the objective is to maximize expected reward while controlling the maximum cost encountered along a trajectory.
The paper introduces Exchange Policy Optimization (EPO), a framework for semi‑infinite safe reinforcement learning that handles infinitely many constraints by iteratively solving finite subproblems. EPO expands or deletes constraints based on tolerance violations and Lagrange multipliers, maintaining computational tractability while converging to an optimal policy with bounded safety violations. The authors prove finite convergence, provide iteration bounds, and quantify the suboptimality gap under mild assumptions.
arXiv:2609. 10866v1 Announce Type: new Abstract: Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating risks in safety-critical applications.
arXiv:2504. 12557v3 Announce Type: replace-cross Abstract: Ensuring safe behavior in reinforcement learning (RL) is challenging when safety constraints are implicit and cannot be densely measured.