Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework

Majid Mazouchi; Subramanya Nageshrao; Hamidreza Modares

doi:10.1109/JAS.2021.1004353

Volume 9 Issue 3

Mar. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(3): 466-481

M. Mazouchi, S. Nageshrao, and H. Modares, “Conflict-aware safe reinforcement learning: A meta-cognitive learning framework,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 466–481, Mar. 2022. doi: 10.1109/JAS.2021.1004353

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M. Mazouchi, S. Nageshrao, and H. Modares, “Conflict-aware safe reinforcement learning: A meta-cognitive learning framework,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 466–481, Mar. 2022. doi: 10.1109/JAS.2021.1004353

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Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework

doi: 10.1109/JAS.2021.1004353

1.
Michigan State University, East Lansing, MI 48824 USA
2.
Ford Research and Innovation Center, Ford Motor Company, Palo Alto, CA 94304 USA

Funds: This work was supported by Ford Motor Company-Michigan State University Alliance

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Author Bio:
Majid Mazouchi received the B.Sc. degree from K. N. Toosi University of Technology, Iran, in 2007, and the M.Sc. and Ph.D. degrees from Ferdowsi University of Mashhad, Iran, in 2010 and 2018, respectively. He was a Senior Lecturer at Semnan University from 2017 to 2018. He is currently a Researcher in Mechanical Engineering Department, Michigan State University, USA. His current research interests include multi-agent systems, safe reinforcement learning, cyber-physical systems, and distributed control. Dr. Mazouchi is a Guest Associate Editor of the Frontiers in Control Engineering: Adaptive, Robust and Fault Tolerant Control, and he also serves as a Reviewer of several international journals and conferences, including IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Control Systems Letters, and IEEE/CAA Journal of Automatica Sinica

Subramanya P. Nageshrao received the B.E. degree in electronics and communication from Visvesvaraya Technological University, India, in 2005, M.S. degree in mechatronics from Technische Universitat Hamburg-Harburg, Germany, in 2011, and the Ph.D. degree from Delft University of Technology, The Netherlands, in 2016. From 2005 to 2009, he was a Software Engineer with Bosch Ltd., India. He is currently a Research Scientist with the Ford Research and Innovation Center, Ford Motor Company, USA. He is actively combining reinforcement-learning techniques with passivity-based control with major focus on autonomous driving. His current research interests include machine learning and nonlinear control

Hamidreza Modares (Senior Member, IEEE) received the B.S. degree from the University of Tehran, Iran, in 2004, the M.S. degree from the Shahrood University of Technology, Iran, in 2006, and the Ph.D. degree from the University of Texas at Arlington, USA, in 2015. He was a Senior Lecturer with the Shahrood University of Technology, from 2006 to 2009 and a Faculty Research Associate with the University of Texas at Arlington, USA from 2015 to 2016. He is currently an Assistant Professor in Mechanical Engineering Department, Michigan State University, USA. His current research interests include cyber-physical systems, reinforcement learning, distributed control, robotics, and machine learning. Dr. Modares was a Recipient of the Best Paper Award from the 2015 IEEE International Symposium on Resilient Control Systems. He is also an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems.
Corresponding author: Hamidreza Modares, e-mail: modaresh@msu.edu
Received Date: 2021-06-26
Revised Date: 2021-08-18
Accepted Date: 2021-09-30

Available Online: 2021-11-02

Abstract

Abstract

In this paper, a data-driven conflict-aware safe reinforcement learning (CAS-RL) algorithm is presented for control of autonomous systems. Existing safe RL results with pre-defined performance functions and safe sets can only provide safety and performance guarantees for a single environment or circumstance. By contrast, the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter. This is achieved by utilizing a bilevel learning control architecture: A higher meta-cognitive layer leverages a data-driven receding-horizon attentional controller (RHAC) to adapt relative attention to different system’s safety and performance requirements, and, a lower-layer RL controller designs control actuation signals for the system. The presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a meta-model or knowledge. More specifically, it leverages a prediction meta-model (PMM) which spans the space of all future meta trajectories using a given finite number of past meta trajectories. RHAC will adapt the system’s aspiration towards performance metrics (e.g., performance weights) as well as safety boundaries to resolve conflicts that arise as mission scenarios develop. This will guarantee safety and feasibility (i.e., performance boundness) of the lower-layer RL-based control solution. It is shown that the interplay between the RHAC and the lower-layer RL controller is a bilevel optimization problem for which the leader (RHAC) operates at a lower rate than the follower (RL-based controller) and its solution guarantees feasibility and safety of the control solution. The effectiveness of the proposed framework is verified through a simulation example.
- Optimal control,
- receding-horizon attentional controller (RHAC),
- reinforcement learning (RL)

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The proposed algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter
The bilevel learning control architecture is utilized
A higher meta-cognitive layer leverages a data-driven receding-horizon attentional controller to adapt the relative attention to different system’s safety and performance requirements
A lower-layer RL controller designs control actuation signals for the system

Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework

doi: 10.1109/JAS.2021.1004353

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通讯作者: 陈斌, bchen63@163.com

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