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1.发明申请公开(公告)号：US20210111988A1
公开(公告)日：2021-04-15
申请号：US16598841
申请日：2019-10-10
IPC分类号： H04L12/751 , H04L12/64 , H04L29/08 , H04W40/26 , H04W40/20 , H04W40/02 , H04W40/24 , H04W84/18
摘要： At a source node, a plurality of packets may be determined for transmission to a destination node in a network comprising a plurality of network nodes. A transmission rate of the plurality of packets from the source node to a neighbor node in the network may be adaptively controlled, based on a determination of a current status of the network by utilizing a plurality of parameters that are estimated via a reinforcement learning routing algorithm. The plurality of parameters include an estimated cost value representing a current cost to transmit the plurality of packets to the destination node via the network. Transmissions from intermediate nodes may also be adaptively deferred based on a determination of a current status of the network by utilizing the plurality of parameters.
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2.发明授权Reinforcement learning-based distributed network routing method utilizing integrated tracking and selective sweeping 有权基于加强学习的分布式网络路由方法，利用集成跟踪和选择性扫描公开(公告)号：US09191304B1
公开(公告)日：2015-11-17
申请号：US13964197
申请日：2013-08-12
IPC分类号： H04L12/28 , H04L12/751
CPC分类号： H04L12/6418 , H04L45/08 , H04W40/20 , H04W40/248 , H04W40/26
摘要： A reinforcement learning-based method is provided that enables efficient communication for networks having varying numbers and topologies of mobile and stationary nodes. The method provides an autonomous, optimized, routing method that may be implemented in a distributed manner among the nodes that allows the nodes to make intelligent decisions of how to forward data from a source node to a destination node with little or no a priori information about the network. The method involves receiving, at a node within a distributed network, data packets containing position and velocity information from a transmitting node. Position and velocity estimates are determined for the transmitting and receiving nodes using the position and velocity information. State-action pair value estimates are determined in the destination direction for forward packets and the source direction for backward sweeping packets, along with associated destination direction and source direction state value estimates, which determine packet transmittal.
摘要翻译： 提供了一种基于强化学习的方法，其能够为具有不同数量和移动和固定节点的拓扑的网络进行有效的通信。 该方法提供了一种自主的，优化的路由方法，其可以在节点之间以分布式方式实现，其允许节点做出关于如何将数据从源节点转发到目的地节点的智能决策，其中很少或没有关于 网络。 该方法包括在分布式网络内的节点处接收包含来自发送节点的位置和速度信息的数据分组。 使用位置和速度信息为发送和接收节点确定位置和速度估计。 在转发分组的目的地方向和反向扫描分组的源方向以及确定分组传送的相关联的目的地方向和源方向状态值估计中确定状态对值估计。
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3.发明授权公开(公告)号：US11146479B2
公开(公告)日：2021-10-12
申请号：US16598841
申请日：2019-10-10
IPC分类号： H04L12/751 , H04L12/64 , H04L29/08 , H04W40/26 , H04W40/02 , H04W40/24 , H04W84/18 , H04W40/20
摘要： At a source node, a plurality of packets may be determined for transmission to a destination node in a network comprising a plurality of network nodes. A transmission rate of the plurality of packets from the source node to a neighbor node in the network may be adaptively controlled, based on a determination of a current status of the network by utilizing a plurality of parameters that are estimated via a reinforcement learning routing algorithm. The plurality of parameters include an estimated cost value representing a current cost to transmit the plurality of packets to the destination node via the network. Transmissions from intermediate nodes may also be adaptively deferred based on a determination of a current status of the network by utilizing the plurality of parameters.
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