arXiv:2501. 15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance.
By Xinyang Wang, Hongwei Zhang, Shimin Wang, Wei Xiao, Martin Guay
arXiv:2609.38016v1 Announce Type: new
Abstract: Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to...
By Huan Rong, Chao Yin, Anouar Imel, Yijie Xia, Tinghuai Ma
arXiv:2602.08054v2 Announce Type: replace-cross
Abstract: Offline reinforcement learning (RL) offers a promising framework for deploying autonomous systems in safety-critical settings without the ris...
By Manan Tayal, Mumuksh Tayal, Ravi Prakash
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.
By Jiaming Zhang, Yujie Yang, Haoning Wang, Liping Zhang, Shengbo Eben Li
Runtime assurance (RTA) protects a safety-critical system by switching from an advanced controller to a verified safe controller when a monitored condition is violated. The standard latching rule, which trips on the first breach of the safe set and then coasts, is correct for a diverging controller but pathological for a capable online-adapting one.
arXiv:2608. 03662v1 Announce Type: new Abstract: Safety shields are runtime enforcement mechanisms that restrict the actions of a controller to guarantee safety.
By Filip Cano, Thomas A. Henzinger, Konstantin Kueffner