Reinforcement learning on a futures market simulator

In recent years, market forecasting by machine learning methods has been flourishing. Most existing works use a past market data set, because they assume that each trader's individual decisions do not affect market prices at all. Meanwhile, there have been attempts to analyze economic phenomena by constructing virtual market simulators, in which human and artificial traders really make trades. Since prices in a market are, in fact, determined by every trader's decisions, a virtual market is more realistic, and the above assumption does not apply. In this work, we design several reinforcement learners on the futures market simulator U-Mart (Unreal Market as an Artificial Research Testbed) and compare our learners with the previous champions of U-Mart competitions empirically.