Many real-world domains require safe decision making in the presence of
uncertainty. In this work, we propose a deep reinforcement learning framework
for approaching this important problem. We consider a risk-averse perspective
towards model uncertainty through the use of coherent distortion risk measures,
and we show that our formulation is equivalent to a distributionally robust
safe reinforcement learning problem with robustness guarantees on performance
and safety. We propose an efficient implementation that only requires access to
a single training environment, and we demonstrate that our framework produces
robust, safe performance on a variety of continuous control tasks with safety
constraints in the Real-World Reinforcement Learning Suite.

我们提出了一个采用深度强化学习的框架，通过相干畸变风险度量考虑模型不确定性的风险规避观点，并表明我们的公式等价于具有性能和安全保障的分布鲁棒安全强化学习问题，并展示了我们框架在 Real-World 强化学习套件中各种具有安全约束的连续控制任务上产生了稳健安全的表现。