Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing

Designers face many system optimization problems when building distributed systems. Traditionally, designers have relied on optimization techniques that require either prior knowledge or centrally managed runtime knowledge of the system's environment, but such techniques are not viable in dynamic networks where topology, resource, and node availability are subject to frequent and unpredictable change. To address this problem, we propose collaborative reinforcement learning (CRL) as a technique that enables groups of reinforcement learning agents to solve system optimization problems online in dynamic, decentralized networks. We evaluate an implementation of CRL in a routing protocol for mobile ad hoc networks, called SAMPLE. Simulation results show how feedback in the selection of links by routing agents enables SAMPLE to adapt and optimize its routing behavior to varying network conditions and properties, resulting in optimization of network throughput. In the experiments, SAMPLE displays emergent properties such as traffic flows that exploit stable routes and reroute around areas of wireless interference or congestion. SAMPLE is an example of a complex adaptive distributed system.