Learning-based Model Predictive Control for Safe Reinforcement Learning

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, realworld 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.