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probabilistic automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2327-2339页

作者： Min Yang Guanjun Liu Ziyuan Zhou Jiacun Wang the Department of Computer Science Tongji University IEEE the Computer Science and Software Engineering Department Monmouth UniversityWest Long Branch

Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model ***, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

关键词： Deep reinforcement learning(DRL) performance improvement framework probabilistic automata real-time monitoring the key probabilistic decision-making units(PDMU)-action pair

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Complete Proof Systems for Amortised probabilistic Bisimulations

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Journal of Computer Science & Technology 2016年第2期31卷 300-316页

作者： Li-Li Xu Hui-Min Lin State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Ecole P olytechnique P alaiseau 91120 France

The notion of amortisation has been integrated in quantitative bisimulations to make long-term behavioral comparisons between nondeterministic systems. In this paper, we present sound and complete proof systems for amortised strong probabilistic bisimulation and its observational congruence on a process algebra with probability and nondeterminism, and prove their soundness and completeness. Our results make it possible to reason about long-term （observable） probabilistic behaviors by syntactic manipulations.

关键词： axiomatization probabilistic calculus for communication systems (CCS) probabilistic automata amortisedbisimulation

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