  Recent technological developments have changed the fundamental ways stock
markets function, bringing regulatory instances to assess the benefits of these
developments. In parallel, the ongoing machine learning revolution and its
multiple applications to trading can now be used to design a next generation of
financial models, and thereby explore the systemic complexity of financial
stock markets in new ways. We here follow on a previous groundwork, where we
designed and calibrated a novel agent-based model stock market simulator, where
each agent autonomously learns to trade by reinforcement learning. In this
Paper, we now study the predictions of this model from a regulator's
perspective. In particular, we focus on how the market quality is impacted by
smaller order book tick sizes, increasingly larger metaorders, and higher
trading frequencies, respectively. Under our model assumptions, we find that
the market quality benefits from the latter, but not from the other two trends.
