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.
The effectiveness and adaptability of RL-based agents within the simulation provide insights into their response to significant market events, enhancing the understanding of market dynamics and participant behaviors.
Understanding how markets react to external and internal events is crucial for investors and regulators, as it can help them make informed decisions in a fast-paced and volatile market.
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.
#market-simulation #reinforcement-learning #agent-based-modeling #financial-markets #investor-behavior
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