Using reinforcement learning to improve network durability

Abstract

Our goal is to determine and optimize the efficacy of reinforcing an existing flow network to prevent unmet demand from imminent disruptions. We are given probabilities of failures for edges in the network and are asked to find edges which will best provide durability to the network post-event. The problem is extended to multiple time steps to address concerns of available resources versus quality of installations: the farther away from the event one makes decisions the more resources are available but the less reliable the uncertainty information. This sequential decision-making process is a classic example of dynamic programming. To avoid the "curses of dimensionality", we formulate an approximate dynamic program. To improve performance, especially as applied to flow networks, we derive several innovative adaptations from reinforcement learning concepts. This involves developing a policy, a function that makes installation decisions when given current forecast information, in a two step process: policy evaluation and policy improvement.