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：校准经济学与金融学中的基于智能体模型（ABMs）通常需要在一个庞大的参数空间中进行无导数搜索。本研究基于真实数据，对宏观经济学中一个知名ABM校准过程中的多种搜索方法进行了基准测试，并进一步评估了由不同方法组合而成的“混合策略”的性能。研究发现，基于随机森林代理模型的方法尤为高效，且组合搜索方法通常能提升性能，因为单一方法的偏差得以缓解。基于这些观察，我们提出了一种强化学习（RL）方案，可在校准过程中自动选择并实时组合搜索方法。该强化学习智能体仅在特定方法持续表现良好时对其进行持续利用，但一旦该方法达到性能瓶颈，便会探索新策略。最终形成的强化学习搜索方案在测试中超越了所有其他方法或其组合，且无需依赖任何先验信息或试错流程。