This paper discusses a novel packet computer network architecture, a “Cognitive Packet Network (CPN)”, in which intelligent capabilities for routing and flow control are moved towards the packets, rather than being concentrated in the nodes. The routing algorithm in CPN uses reinforcement learning based on the Random Neural Network. We outline the design of CPN and show how it incorporates packet loss and delay directly into user Quality of Service (QoS) criteria, and use these criteria to conduct routing. We then present our experimental test-bed and report on extensive measurement experiments. These experiments include measurements of the network under link and node failures. They illustrate the manner in which neural network based CPN can be used to support a reliable adaptive network environment for peer-to-peer communications over an unreliable infrastructure.