Skill-based Safe Reinforcement Learning with Risk Planning
This addresses safety concerns for RL agents in robotics, though it appears incremental by building on existing safe RL methods with a novel planning component.
The paper tackles the problem of ensuring safety in reinforcement learning agents interacting with real-world environments by proposing a Safe Skill Planning approach that uses offline demonstration data to predict skill risks and plan risk-averse policies. The results show it consistently outperforms previous state-of-the-art safe RL methods in benchmark robotic simulations.
Safe Reinforcement Learning (Safe RL) aims to ensure safety when an RL agent conducts learning by interacting with real-world environments where improper actions can induce high costs or lead to severe consequences. In this paper, we propose a novel Safe Skill Planning (SSkP) approach to enhance effective safe RL by exploiting auxiliary offline demonstration data. SSkP involves a two-stage process. First, we employ PU learning to learn a skill risk predictor from the offline demonstration data. Then, based on the learned skill risk predictor, we develop a novel risk planning process to enhance online safe RL and learn a risk-averse safe policy efficiently through interactions with the online RL environment, while simultaneously adapting the skill risk predictor to the environment. We conduct experiments in several benchmark robotic simulation environments. The experimental results demonstrate that the proposed approach consistently outperforms previous state-of-the-art safe RL methods.