Weight initialization for random neural network reinforcement learning

An apparatus comprising:

• a network interface configured to communicate over a network;
• and
• one or more processors, wherein the one or more processors are configured to:
• determine a plurality of paths from a source network connected device to a destination network connected device through the network for transmitting a packet from the source network connected device to the destination network connected device;
• model the plurality of paths as a plurality of nodes in a Random Neural Network, wherein each of the plurality of nodes corresponds to one of the plurality of paths;
• calculate a reward for each of the plurality of nodes;
• determine an excitatory weight and an inhibitory weight for each of the plurality of nodes in the Random Neural Network, wherein determining the excitatory weight comprises setting the excitatory weight for a node of the plurality of nodes for which the excitatory weight is being determined directly proportional to the reward corresponding to the node of the plurality of nodes for which the excitatory weight is being determined, and wherein determining the inhibitory weight comprises setting the inhibitory weight for a node of the plurality of nodes for which the inhibitory weight is being determined inversely proportional to the reward corresponding to the node of the plurality of nodes for which the inhibitory weight is being determined;
• determine a potential for each of the plurality of nodes based upon the excitatory weights and the inhibitory weights for the plurality of nodes;
• selecting a path of the plurality of paths corresponding to the node with a highest potential;
• and
• transmit, via the network interface, the packet from the source network connected device to the destination network connected device over the path of the plurality of paths corresponding to the node with the highest potential.

A non-transitory computer readable storage medium encoded with instructions, wherein the instructions, when executed, are operable to:

• determine a plurality of paths from a source network connected device to a destination network connected device through a network for transmitting a packet from the source network connected device to the destination network connected device;
• model the plurality of paths as a plurality of nodes in a Random Neural Network, wherein each of the plurality of nodes corresponds to one of the plurality of paths;
• calculate a reward for each of the nodes of the plurality of nodes;
• determine an excitatory weight and an inhibitory weight for each of the plurality of nodes in the Random Neural Network, wherein determining the excitatory weight comprises setting the excitatory weight for a node of the plurality of nodes for which the excitatory weight is being determined directly proportional to the reward corresponding to the node of the plurality of nodes for which the excitatory weight is being determined, and wherein determining the inhibitory weight comprises setting the inhibitory weight for a node of the plurality of nodes for which the inhibitory weight is being determined inversely proportional to the reward corresponding to the node of the plurality of nodes for which the inhibitory weight is being determined;
• determine a potential for each of the plurality of nodes based upon the excitatory weights and the inhibitory weights for the plurality of nodes;
• select a path of the plurality of paths corresponding to the node with a highest potential;
• and
• transmit the packet from the source network connected device to the destination network connected device over the path of the plurality of paths corresponding to the node with the highest potential.