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This paper introduces Deep-BQRL, a model-free distributional reinforcement learning framework that extends buffered-quantile learning to neural function approximation for risk-sensitive decision-making, showing improved performance over existing methods in specific experiments.
This paper extends certification methods to risk-sensitive reinforcement learning, establishing lower bounds on expected rewards under state adversarial perturbations via convex optimization and demonstrating improved certified bounds with risk-averse training.
RS-Diffuser 提出了一种风险敏感的离线扩散规划框架，结合扩散轨迹生成和分布值批评器，通过尾部感知目标在推理时灵活调整风险偏好，在安全关键任务中提升了回报和鲁棒性。
This paper introduces a simple yet powerful methodology for Utility-Constrained MDPs (UCMDPs) that enables risk-sensitive constraints without fixing constraint limits in advance, outperforming baselines on Safety Gymnasium benchmarks.