Safety-Aware Reinforcement Learning Framework with an Actor-Critic-Barrier Structure
This paper considers the control problem with constraints on full-state and control input simultaneously. First, a novel barrier function based system transformation approach is developed to guarantee the full-state constraints. To deal with the input saturation, the hyperbolic-type penalty function is imposed on the control input. The actor-critic based reinforcement learning technique is combined with the barrier transformation to learn the optimal control policy that considers both the full-state constraints and input saturations. To illustrate the efficacy, a numeric simulation is implemented in the end.
Y. Yang et al., "Safety-Aware Reinforcement Learning Framework with an Actor-Critic-Barrier Structure," Proceedings of the American Control Conference (2019, Philadelphia, PA), pp. 2352 - 2358, Institute of Electrical and Electronics Engineers (IEEE), Jul 2019.
The definitive version is available at https://doi.org/10.23919/ACC.2019.8815335
2019 American Control Conference, ACC 2019 (2019: Jul. 10-12, Philadelphia, PA)
Electrical and Computer Engineering
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Full-State Constraints; Input Saturation; Reinforcement Learning; Safe Control
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2019 American Automatic Control Council, All rights reserved.
01 Jul 2019