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

IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 466-481页

作者： majid mazouchi Subramanya Nageshrao Hamidreza Modares Michigan State University East LansingMI 48824 USA Ford Research and Innovation Center Ford Motor CompanyPalo AltoCA 94304 USA IEEE

In this paper,a data-driven conflict-aware safe reinforcement learning(CAS-RL)algorithm is presented for control of autonomous *** safe RL results with predefined performance functions and safe sets can only provide safety and performance guarantees for a single environment or *** contrast,the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might *** is achieved by utilizing a bilevel learning control architecture:A higher metacognitive 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 *** presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a metamodel or *** 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 *** 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 *** will guarantee safety and feasibility(i.e.,performance boundness)of the lower-layer RL-based control *** 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 *** effectiveness of the proposed framework is verified through a simulation example.

关键词： Optimal control receding-horizon attentionalcontroller(RHAC) reinforcement learning(RL)

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A Novel Distributed Optimal Adaptive Control Algorithm for Nonlinear Multi-Agent Differential Graphical Games

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 331-341页

作者： majid mazouchi Mohammad Bagher Naghibi-Sistani Seyed Kamal Hosseini Sani Department of Electrical Engineering Ferdowsi University of Mashhad

In this paper, an online optimal distributed learning algorithm is proposed to solve leader-synchronization problem of nonlinear multi-agent differential graphical games. Each player approximates its optimal control policy using a single-network approximate dynamic programming(ADP) where only one critic neural network(NN) is employed instead of typical actorcritic structure composed of two NNs. The proposed distributed weight tuning laws for critic NNs guarantee stability in the sense of uniform ultimate boundedness(UUB) and convergence of control policies to the Nash equilibrium. In this paper, by introducing novel distributed local operators in weight tuning laws, there is no more requirement for initial stabilizing control policies. Furthermore, the overall closed-loop system stability is guaranteed by Lyapunov stability analysis. Finally, Simulation results show the effectiveness of the proposed algorithm.

关键词： Approximate dynamic programming(ADP) distributed control neural networks(NNs) nonlinear differentia graphical games optimal control

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