  Agent-based Models (ABMs) are valuable tools for policy analysis. ABMs help
analysts explore the emergent consequences of policy interventions in
multi-agent decision-making settings. But the validity of inferences drawn from
ABM explorations depends on the quality of the ABM agents' behavioral models.
Standard specifications of agent behavioral models rely either on heuristic
decision-making rules or on regressions trained on past data. Both prior
specification modes have limitations. This paper examines the value of
reinforcement learning (RL) models as adaptive, high-performing, and
behaviorally-valid models of agent decision-making in ABMs. We test the
hypothesis that RL agents are effective as utility-maximizing agents in policy
ABMs. We also address the problem of adapting RL algorithms to handle
multi-agency in games by adapting and extending methods from recent literature.
We evaluate the performance of such RL-based ABM agents via experiments on two
policy-relevant ABMs: a minority game ABM, and an ABM of Influenza
Transmission. We run some analytic experiments on our AI-equipped ABMs e.g.
explorations of the effects of behavioral heterogeneity in a population and the
emergence of synchronization in a population. The experiments show that RL
behavioral models are effective at producing reward-seeking or
reward-maximizing behaviors in ABM agents. Furthermore, RL behavioral models
can learn to outperform the default adaptive behavioral models in the two ABMs
examined.
