Deadlock-Detection via Reinforcement Learning

Abstract

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.

A New Method for Solving Deadlock Using Genetic Algorithms

“…The present method observes a superior result due to integration of the first buffer, first serve policy in the system. In [11,12] a new scheduling method presented by the researchers. The method combines a robust supervised control with heretic search.…”