ISSN: 2319-7293
+44-77-2385-9429
University of Central Florida, Orlando, USA
Extended Abstract
Deadlock-Detection via Reinforcement Learning.
Author(s): Chen M
Optimization of makespan in scheduling is a highly desirable research topic, deadlock detection and prevention is one of the fundamental issues. Supported by what learned from this class, a reinforcement learning approach is developed to unravel this optimization difficulty. By evaluating this RL model on forty classical non-buffer benchmarks and compare with other alternative algorithms, we presented a near-optimal result.
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