Title page for ETD etd-08242003-224906


Type of Document Master's Thesis
Author Shiraev, Dmitry Eric
URN etd-08242003-224906
Title Inverse Reinforcement Learning and Routing Metric Discovery
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Ramakrishnan, Naren Committee Co-Chair
Varadarajan, Srinidhi Committee Co-Chair
Ribbens, Calvin J. Committee Member
Keywords
  • Inverse Reinforcement Learning
  • Routing
  • Network Metrics
Date of Defense 2003-08-22
Availability unrestricted
Abstract
Uncovering 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.

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