Safety Verification of Non-Deterministic Policies in Reinforcement Learning

Reinforcement Learning represents a powerful paradigm in artificial intelligence, enabling agents to learn optimal behaviors through interactions with their environment. However, ensuring the safety of policies learned in non-deterministic environments, where outcomes are inherently uncertain and va...

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Bibliographic Details
Main Authors: Ryeonggu Kwon, Gihwon Kwon
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10786219/
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