I am a graduate student in economics at Princeton University. I am interested in dynamic heterogeneous-agent spatial models and reinforcement learning techniques to solve them.
I present a new package which aims to automate the process of using reinforcement learning to solve discrete-time heterogeneous-agent macroeconomic models. Models with discrete choice, matching, aggregate uncertainly, and multiple locations are supported. The pure-Julia package, tentatively named Bucephalus.jl, also defines a data structure for describing this class of models, allowing new solvers to be easily implemented and models to be defined once and solved many ways.