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Welcome to the RLRP wiki page.

This wiki presents a documentation and related information for RLRP – Reinforcement Learning Routing Protocol. RLRP is a multi-hop routing protocol implementation for wireless and wired ad hoc networks with either static or dynamic topologies, used in various communication scenarios in multi-hop networks, such as sensor networks, mesh networks, static outdoor networks, mobile ad hoc networks (MANET), vehicular ad hoc networks (VANET) and so on.

The main idea of RLRP is to apply a Reinforcement Learning mechanism from Machine Learning theory to a problem of decision-making for further packet forwarding actions. The RLRP is based on dynamic calculation of some "estimation" value for the given packet destination address. Those values are being dynamically modified by means of so-called "reward mechanism", which represents some feedback values for the chosen "actions" - a packet forward events to the selected next-hop node. This routing protocol works on Linux-based machines with TCP/IP stack, and provides the routing functionality for any data packets with either IPv4 or IPv6 addressing.