Shiraev, Dmitry Eric2014-03-142014-03-142003-08-22etd-08242003-224906http://hdl.handle.net/10919/34728Uncovering the metrics and procedures employed by an autonomous networking system is an important problem with applications in instrumentation, traffic engineering, and game-theoretic studies of multi-agent environments. This thesis presents a method for utilizing inverse reinforcement learning (IRL)techniques for the purpose of discovering a composite metric used by a dynamic routing algorithm on an Internet Protocol (IP) network. The network and routing algorithm are modeled as a reinforcement learning (RL) agent and a Markov decision process (MDP). The problem of routing metric discovery is then posed as a problem of recovering the reward function, given observed optimal behavior. We show that this approach is empirically suited for determining the relative contributions of factors that constitute a composite metric. Experimental results for many classes of randomly generated networks are presented.In CopyrightInverse Reinforcement LearningRoutingNetwork MetricsThesishttp://scholar.lib.vt.edu/theses/available/etd-08242003-224906/