Titlebook: Quantitative Evaluation of Systems; 18th International C Alessandro Abate,Andrea Marin Conference proceedings 2021 Springer Nature Switzerl

不斷的變動

DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learningn learning action policies in complex and dynamic environments. Despite this success however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes . rewards, which may disregard rare but critical situations and hence lack local

不可磨滅

窒息

Safe Learning for Near-Optimal Schedulingulers for a preemptible task scheduling problem. Our algorithms can handle Markov decision processes (MDPs) that have . states and beyond which cannot be handled with state-of-the art probabilistic model-checkers. We provide probably approximately correct (PAC) guarantees for learning the model. Add

dysphagia

Performance Evaluation: Model-Driven or?Problem-Driven?necting, and that will result in a better uptake of the newest techniques and tools in the field of design of computer and communication systems. Following these recommendations will probably push scientists a little out of their comfort zone, however, I feel the potential extra reward of seeing our work truly applied is more than worth it.

Intend

boisterous

SEH: Size Estimate Hedging for Single-Server Queuesrocessing times for scheduling decisions. A job’s priority is increased dynamically according to an SRPT rule until it is determined that it is underestimated, at which time the priority is frozen. Numerical results suggest that SEH has desirable performance for estimation error variance that is consistent with what is seen in practice.

使無效

Safe Learning for Near-Optimal Schedulingitionally, we extend Monte-Carlo tree search with advice, computed using safety games or obtained using the earliest-deadline-first scheduler, to safely explore the learned model online. Finally, we implemented and compared our algorithms empirically against shielded deep .-learning on large task systems.

Petechiae

Exaggerate

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