Market Model Benchmark Suite for Machine Learning Techniques

摘要

Recent developments in deep-reinforcement learning have yielded promising results in artificial games and test domains. To explore opportunities and evaluate the performance of these machine learning techniques, various benchmark suites are available, such as the Arcade Learning Environment, rllab, OpenAI Gym, and the StarCraft II Learning Environment. This set of benchmark suites is extended with the open business simulation model described here, which helps to promote the use of machine learning techniques as valueadding tools in the context of strategic decision making and economic model calibration and harmonization. The benchmark suite extends the current state-of-the-art problems for deep-reinforcement learning by offering an infinite state and action space for multiple players in a non-zero-sum game environment of imperfect information. It provides a model that can be characterized as both a credit assignment problem and an optimization problem. Experiments with this suite?s deep-reinforcement learning algorithms, which yield remarkable results for various artificial games, highlight that stylized market behavior can be replicated, but the infinite action space, simultaneous decision making, and imperfect information pose a computational challenge. With the directions provided, the benchmark suite can be used to explore new solutions in machine learning for strategic decision making and model calibration.