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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

February 1, 2023

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Authors

Richard Cheng

California Institute of Technology


Gábor Orosz

University of Michigan


Richard M. Murray

California Institute of Technology


Joel W. Burdick

California Institute of Technology


DOI:

10.1609/aaai.v33i01.33013387


Abstract:

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties.Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

Topics: AAAI

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HOW TO CITE:

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks Proceedings of the AAAI Conference on Artificial Intelligence (2019) 3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks AAAI 2019, 3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick (2019). End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. Proceedings of the AAAI Conference on Artificial Intelligence, 3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick. 2019. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. "Proceedings of the AAAI Conference on Artificial Intelligence". 3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick. (2019) "End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks", Proceedings of the AAAI Conference on Artificial Intelligence, p.3387-3395

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick, "End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks", AAAI, p.3387-3395, 2019.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick. "End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick. "End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 3387-3395.

Richard Cheng||Gábor Orosz||Richard M. Murray||Joel W. Burdick. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. AAAI[Internet]. 2019[cited 2023]; 3387-3395.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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