Multi-agent reinforcement learning with heterogeneous preferences leads to emergent role specialization whose interactions produce fat-tailed returns and volatility clustering, offering a computational realization of the Adaptive Market Hypothesis.
B Our Method: Training Details In our experiment, the shared-policy was trained using proximal policy opti- mization [Schulman et al., 2017]
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Financial Market as a Self-Organized Ecosystem: Simulation via Learning with Heterogeneous Preferences
Multi-agent reinforcement learning with heterogeneous preferences leads to emergent role specialization whose interactions produce fat-tailed returns and volatility clustering, offering a computational realization of the Adaptive Market Hypothesis.