Learning-based Model Predictive Control for Safe Reinforcement LearningDownload PDF

Torsten Koller, Felix Berkenkamp, Matteo Turchetta, Joschka Bödecker, Andreas Krause

28 May 2019 (modified: 05 May 2023)RSS 2019Readers: Everyone
Abstract: Reinforcement learning has been successfully used to solve difficult tasks in complex unknown environments. However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications. In this paper, we attempt to bridge the gap between learning-based techniques that are scalable and highly autonomous but often unsafe and robust control techniques, which have a solid theoretical foundation that guarantees safety but often require extensive expert knowledge to identify the system and estimate disturbance sets. We combine a provably safe learning-based MPC scheme that allows for input-dependent uncertainties with techniques from model-based RL to solve tasks with only limited prior knowledge. We evaluate the resulting algorithm to solve a reinforcement learning task in a simulated cart-pole dynamical system with safety constraints.
0 Replies

Loading