  Over the past decades, breakthroughs such as Reinforcement Learning (RL) and
Agent-based modeling (ABM) have made simulations of economic models feasible.
Recently, there has been increasing interest in applying ABM to study the
impact of residential preferences on neighborhood segregation in the Schelling
Segregation Model. In this paper, RL is combined with ABM to simulate a
modified Schelling Segregation model, which incorporates moving expenses as an
input parameter. In particular, deep Q network (DQN) is adopted as RL agents'
learning algorithm to simulate the behaviors of households and their
preferences. This paper studies the impact of moving expenses on the overall
segregation pattern and its role in social integration. A more comprehensive
simulation of the segregation model is built for policymakers to forecast the
potential consequences of their policies.
