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

The impact of moving expenses on social segregation: a simulation with RL and ABM