Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties.

ABSTRACT: Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In general, safety guarantees are critical in reinforcement learning when the system is safety-critical and/or task restarts are not practically feasible. In optimal control theory, safety requirements are often expressed in terms of state and/or control constraints. In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints. To soften the excitation requirements, model-based reinforcement learning methods that rely o

SUBMITTER: Mahmud SMN 

PROVIDER: S-EPMC8717089 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature