Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees 事件

Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees arXiv:2606.04812v1 Announce Type: cross Abstract: Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour. A method of policy verification is to construct probabilistic barrier-certificates by sampli