Inverse Reinforcement Learning and Routing Metric Discovery
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.
Collections
- Masters Theses [18654]