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- W1497297211 abstract "Actually, various kinds of sources (such as voice, video, or data) with diverse traffic characteristics and quality of service requirements (QoS), which are multiplexed at very high rates, leads to significant traffic problems such as packet losses, transmission delays, delay variations, etc, caused mainly by congestion in the networks. The prediction of these problems in real time is quite difficult, making the effectiveness of traditional methodologies based on analytical models questionable. Effective network routing means selecting the optimal communication paths. It can be modeled as a multiagent RL problem. We propose an adaptive routing and scheduling algorithm based on reinforcement learning techniques." @default.
- W1497297211 created "2016-06-24" @default.
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- W1497297211 date "2004-11-13" @default.
- W1497297211 modified "2023-10-01" @default.
- W1497297211 title "A reinforcement learning approach for routing and scheduling packets in dynamic networks" @default.
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- W1497297211 doi "https://doi.org/10.1109/ictta.2004.1307945" @default.
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