Reinforcement Learning in Agent-Based Market Simulation: Unveiling
  Realistic Stylized Facts and Behavior
Zhiyuan Yao Zheng Li Matthew Thomas Ionut Florescu
Investors and regulators can greatly benefit from a realistic market
simulator that enables them to anticipate the consequences of their decisions
in real markets. However, traditional rule-based market simulators often fall
short in accurately capturing the dynamic behavior of market participants,
particularly in response to external market impact events or changes in the
behavior of other participants. In this study, we explore an agent-based
simulation framework employing reinforcement learning (RL) agents. We present
the implementation details of these RL agents and demonstrate that the
simulated market exhibits realistic stylized facts observed in real-world
markets. Furthermore, we investigate the behavior of RL agents when confronted
with external market impacts, such as a flash crash. Our findings shed light on
the effectiveness and adaptability of RL-based agents within the simulation,
offering insights into their response to significant market events.