Safe reinforcement learning (RL) with assured satisfaction of hard state constraints during training has recently received a lot of attention. Safety filters, e.g., based on control barrier functions (CBFs), provide a promising way for safe RL via modifying the unsafe actions of an RL agent on the fly. Existing safety filter-based approaches typically involve learning of uncertain dynamics and quantifying the learned model error, which leads to conservative filters before a large amount of data is collected to learn a good model, thereby preventing efficient exploration. This paper presents a method for safe and efficient model-free RL using disturbance observers (DOBs) and control barrier functions (CBFs). Unlike most existing safe RL methods that deal with hard state constraints, our method does not involve model learning, and leverages DOBs to accurately estimate the pointwise value of the uncertainty, which is then incorporated into a robust CBF condition to generate safe actions. The DOB-based CBF can be used as a safety filter with any model-free RL algorithms by minimally modifying the actions of an RL agent whenever necessary to ensure safety throughout the learning process. Simulation results on a unicycle and a 2D quadrotor demonstrate that the proposed method outperforms a state-of-the-art safe RL algorithm using CBFs and Gaussian processes-based model learning, in terms of safety violation rate, and sample and computational efficiency.
翻译:安全过滤器,例如基于控制屏障功能(CBFs)的安全过滤器,通过修改飞行中的RL代理器的不安全行动,为安全过滤器提供了一个有希望的方法。现有的基于安全过滤器的方法通常包括学习不确定的动态和量化所学模型错误,这导致在收集大量数据之前采用保守的过滤器,以学习一个好模型,从而防止有效的探索。本文件介绍了使用扰动观察员(DBs)和控制屏障功能(CBFs)安全和高效无模型的RL方法。与大多数现有的处理硬性限制的安全过滤器方法不同,我们的方法并不涉及模型学习,而是利用DBOBs准确估计不确定性的点值,然后将其纳入一个强有力的CBFF条件,以产生安全行动。基于DAB的CBFFFB可以使用任何基于模型的无模式的RL算法作为安全过滤器,必要时尽可能降低RL代理器的行动,以确保整个学习过程中的安全。在拟议的安全性成本标准、成本标准、成本标准、成本标准、标准、标准、标准、标准标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准、标准