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.

翻译：在经济和金融领域校准基于智能体模型（ABM）通常涉及在非常大的参数空间中进行无导数搜索。本研究在真实数据上对一个著名的宏观经济ABM进行校准，对多种搜索方法进行基准测试，并进一步评估通过组合不同方法形成的"混合策略"的性能。我们发现基于随机森林代理模型的方法特别高效，且组合搜索方法通常能提升性能，因为任何单一方法的偏差都得到了缓解。基于这些观察，我们提出一种强化学习（RL）方案，在校准运行期间自动实时选择和组合搜索方法。RL智能体仅在特定方法持续表现良好时持续利用该方法，但一旦该方法达到性能瓶颈，便会探索新策略。由此产生的RL搜索方案在性能上超越了所有测试的其他方法或方法组合，且无需依赖任何先验信息或试错过程。