Inverse Reinforcement Learning and Routing Metric Discovery

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2003

Author dc:creator

• Shiraev, Dmitry Eric

Chairs dc:contributor.committeechair

• Varadarajan, Srinidhi
• Ramakrishnan, Naren

Committee member dc:contributor.committeemember

• Ribbens, Calvin J.

• Inverse Reinforcement Learning
• Routing
• Network Metrics

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-08242003-224906

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/34728