Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly conservative, which impedes exploration and restrains the overall performance. In many realistic tasks, e.g. autonomous driving, large-scale expert demonstration data are available. We argue that extracting expert policy from offline data to guide online exploration is a promising solution to mitigate the conserveness issue. Large-capacity models, e.g. decision transformers (DT), have been proven to be competent in offline policy learning. However, data collected in real-world scenarios rarely contain dangerous cases (e.g., collisions), which makes it prohibitive for the policies to learn safety concepts. Besides, these bulk policy networks cannot meet the computation speed requirements at inference time on real-world tasks such as autonomous driving. To this end, we propose Guided Online Distillation (GOLD), an offline-to-online safe RL framework. GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms. Experiments in both benchmark safe RL tasks and real-world driving tasks based on the Waymo Open Motion Dataset (WOMD) demonstrate that GOLD can successfully distill lightweight policies and solve decision-making problems in challenging safety-critical scenarios.

翻译：安全强化学习旨在寻找在满足成本约束的同时实现高回报的策略。当从零开始学习时，安全强化学习智能体往往过于保守，这阻碍了探索并限制了整体性能。在众多现实任务中（例如自动驾驶），可获取大规模专家示范数据。我们认为从离线数据中提取专家策略以指导在线探索，是缓解保守性问题的有效方案。大容量模型（如决策变换器）已被证明在离线策略学习中表现出色。然而，现实场景采集的数据极少包含危险案例（如碰撞），这使得策略难以学习安全概念。此外，这些庞大策略网络在自动驾驶等现实任务推理时无法满足计算速度要求。为此，我们提出引导式在线蒸馏（GOLD）——一种离在线安全强化学习框架。GOLD通过引导式安全在线强化学习训练，将离线决策变换器策略蒸馏为轻量级策略网络，其表现优于离线决策变换器策略和在线安全强化学习算法。基于Waymo开放运动数据集（WOMD）的基准安全强化任务与现实驾驶任务实验表明，GOLD能成功蒸馏轻量级策略，并解决具有挑战性的安全关键场景中的决策问题。