  Different classes of safe reinforcement learning algorithms have shown
satisfactory performance in various types of safety requirement scenarios.
However, the existing methods mainly address one or several classes of specific
safety requirement scenario problems and cannot be applied to arbitrary safety
requirement scenarios. In addition, the optimization objectives of existing
reinforcement learning algorithms are misaligned with the task requirements.
Based on the need to address these issues, we propose $\mathrm{E^{2}CFD}$, an
effective and efficient cost function design framework. $\mathrm{E^{2}CFD}$
leverages the capabilities of a large language model (LLM) to comprehend
various safety scenarios and generate corresponding cost functions. It
incorporates the \textit{fast performance evaluation (FPE)} method to
facilitate rapid and iterative updates to the generated cost function. Through
this iterative process, $\mathrm{E^{2}CFD}$ aims to obtain the most suitable
cost function for policy training, tailored to the specific tasks within the
safety scenario. Experiments have proven that the performance of policies
trained using this framework is superior to traditional safe reinforcement
learning algorithms and policies trained with carefully designed cost
functions.
