Probabilistic Safeguard for Reinforcement Learning Using Safety Index Guided Gaussian Process Models
Year: 2022
Type: preprint
Abstract: Safety is one of the biggest concerns to applying reinforcement learning (RL) to the physical world. In its core part, it is challenging to ensure RL agents persistently satisfy a hard state constraint without white-box or black-box dynamics models. This paper presents an integrated model learning and safe control framework to safeguard any agent, where its dynamics are learned as Gaussian processes. The proposed theory provides (i) a novel method to construct an offline dataset for model learning that best achieves safety requirements; (ii) a parameterization rule for safety index to ensure the existence of safe control; (iii) a safety guarantee in terms of probabilistic forward invariance when the model is learned using the aforementioned dataset. Simulation results show that our framework guarantees almost zero safety violation on various continuous control tasks.
Source: arXiv (Cornell University)
Authors: Zhao, Weiye, He, Tairan, Liu, Changliu
Institution: Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4302013079