A coflow scheduling method based on deep reinforcement learning
A technology of reinforcement learning and scheduling methods, applied in digital transmission systems, data exchange networks, biological neural network models, etc., can solve problems such as limiting the final effect of scheduling, suboptimal thresholds, complex network environments, etc., and achieve flexible scheduling strategies , Reduce overhead and improve processing efficiency
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[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0048] The present invention is based on the working principle of deep reinforcement learning. Among them, reinforcement learning is an important branch of machine learning. In reinforcement learning, there is an agent for interacting with the environment. The agent takes corresponding actions by observing the state of the environment to obtain the maximum reward value feedback. Reinforcement learning is usually formulated using a ...
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