  In the past, financial stock markets have been studied with previous
generations of multi-agent systems (MAS) that relied on zero-intelligence
agents, and often the necessity to implement so-called noise traders to
sub-optimally emulate price formation processes. However recent advances in the
fields of neuroscience and machine learning have overall brought the
possibility for new tools to the bottom-up statistical inference of complex
systems. Most importantly, such tools allows for studying new fields, such as
agent learning, which in finance is central to information and stock price
estimation. We present here the results of a new generation MAS stock market
simulator, where each agent autonomously learns to do price forecasting and
stock trading via model-free reinforcement learning, and where the collective
behaviour of all agents decisions to trade feed a centralised double-auction
limit order book, emulating price and volume microstructures. We study here
what such agents learn in detail, and how heterogenous are the policies they
develop over time. We also show how the agents learning rates, and their
propensity to be chartist or fundamentalist impacts the overall market
stability and agent individual performance. We conclude with a study on the
impact of agent information via random trading.
