Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations 事件

Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations arXiv:2606.10448v1 Announce Type: cross Abstract: The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor-Critic (SAC). Specifically, noisy state representations may produce unreliable Q-value estimates, and bootstrapping amplifies these errors, forming a failure mode we call the "Financial Entropy Trap". In this p