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Interval Markov Decision Processes with Continuous Action-Spaces
Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals.
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives
Policy robustness in Reinforcement Learning may not be desirable at any cost: the alterations caused by robustness requirements from otherwise optimal policies should be explainable, quantifiable and formally verifiable.
The Wireless Control Bus: Enabling Efficient Multi-hop Event-Triggered Control with Concurrent Transmissions
Event-triggered control (ETC) holds the potential to significantly improve the efficiency of wireless networked control systems.
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