Return

%

signals/second analyzed.

*Expected Return = E(R) = Σ (Ri * Pi)

The accuracy of the model is being monitored on a regular basis.(15-minute period)

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We present a new approach that merges reinforcement learning with game theory for understanding strategic interactions between neural network agents. Our model leverages decision functions, rooted in game theory, to guide its initial learning process. This is then followed by supervised fine-tuning for deeper comprehension. This approach allows us to analyze the strategic behaviors exhibited by agents within a neural network environment.


R : S × A → ℝ


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