  Calibrating agent-based models (ABMs) in economics and finance typically
involves a derivative-free search in a very large parameter space. In this
work, we benchmark a number of search methods in the calibration of a
well-known macroeconomic ABM on real data, and further assess the performance
of "mixed strategies" made by combining different methods. We find that methods
based on random-forest surrogates are particularly efficient, and that
combining search methods generally increases performance since the biases of
any single method are mitigated. Moving from these observations, we propose a
reinforcement learning (RL) scheme to automatically select and combine search
methods on-the-fly during a calibration run. The RL agent keeps exploiting a
specific method only as long as this keeps performing well, but explores new
strategies when the specific method reaches a performance plateau. The
resulting RL search scheme outperforms any other method or method combination
tested, and does not rely on any prior information or trial and error
procedure.
