← Back to all news
arXiv Machine Learning By Samuel Tetteh, Cody Fleming

Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable?

Read the original on arXiv Machine Learning →

The paper investigates safe reinforcement learning by focusing on the episodic-cost tail rather than just the mean cost. It introduces the use of γαρα_{0.1} (the average cost of the worst 10% of episodes) to classify policies as tail-safe. The authors evaluate standard algorithms on Safety-Gymnasium tasks, identify tail-unsafe policies, and explore constraint families to control tail violations while maintaining return.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Sep 15

Evaluation Metrics for Safe Reinforcement Learning

The paper introduces new evaluation metrics for safe reinforcement learning that go beyond average safety guarantees by examining how often and how severely safety bounds are violated, consistency across tasks and bounds, and the relationship between training-time and final policy behavior. It also proposes a safety tier system for categorizing algorithms and presents empirical safety evaluations on multiple navigation tasks. The authors recommend reporting aggregate metrics, distributional data, and task‑specific results together, and provide an open‑source suite, SafeRLEval, to facilitate reliable safety assessment.

By Lindsay Spoor, Aske Plaat, Thomas Moerland
agentsreinforcement-learningbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 21

Provably Optimal Reinforcement Learning under Safety Filtering

The paper proves that using a permissive safety filter in reinforcement learning does not compromise asymptotic performance. By formalizing safety through a safety‑critical Markov decision process and a filtered MDP, the authors show that optimal policies in the filtered MDP achieve the same return as the best safe policy in the original setting. Experiments on Safety Gymnasium confirm zero violations during training and performance that matches or exceeds unfiltered baselines.

By Donggeon David Oh, Duy P. Nguyen, Haimin Hu, Jaime Fern\'andez Fisac
reinforcement-learningsafety
More like this →
arXiv Machine Learning
Oct 9

FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems

FAITH is a model‑free reinforcement learning framework that separates safety from task performance by using a learned safety filter. The filter approximates the optimal state‑action safety value with a feedforward network, allowing the task policy to optimize return through filtered dynamics without a competing safety term. Experiments on a double integrator, Safety Gym, a 29‑DoF humanoid, and a real Unitree G1 show that FAITH achieves high returns while maintaining near‑perfect safety and handling infeasible starts gracefully.

By Songyuan Zhang, Baljeet Singh, Sarthak Ranjeet Kaingade, Chuchu Fan, Bryan Trinh
reinforcement-learningsafety
More like this →