arXiv Machine Learning
Deep catastrophic-risk-aware reinforcement learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 30
On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learning
arXiv:2405.14749v3 Announce Type: replace-cross Abstract: Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL...
arXiv Machine Learning
Jun 30
Constrained Policy Optimization with Cantelli-Bounded Value-at-Risk
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.
arXiv Machine Learning
Sep 3
Cantelli Constrained Policy Optimization
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 Machine Learning
Jun 2
Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
arXiv:2606. 00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-stationary and adversarial environments.
arXiv Machine Learning
Sep 11
Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations
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 Machine Learning
Oct 9
Credal Machine Learning for Risk-Averse Decision Making
The paper introduces a method for risk‑averse machine learning that uses credal sets—collections of probability distributions—to capture epistemic uncertainty about the true loss distribution. By producing predictions as credal sets and applying a novel decision rule that selects a single predictive distribution for conditional value‑at‑risk (CVaR) minimization, the approach aims to reliably avoid catastrophic decisions across classification, distribution shift, and reinforcement learning scenarios while maintaining near‑optimal expected performance.