R-NaD
A reinforcement learning algorithm proposed by DeepMind. By introducing a regularization term into the training objective, R-NaD (Regularized Nash Dynamics) addresses the problem of cyclical strategy oscillation in self-play, enabling stable convergence toward Nash equilibrium.
Timeline (last 90 days)
DeepNash's core algorithm is based on the R-NaD (Regularized Nash Dynamics) method.
R-NaD introduces a regularization term into the training objective to provide a mathematically guaranteed convergence direction for policy evolution, enabling the self-play process to stably approximate a Nash equilibrium.
All Facts (2)
DeepNash's core algorithm is based on the R-NaD (Regularized Nash Dynamics) method.
50%UnverifiedR-NaD introduces a regularization term into the training objective to provide a mathematically guaranteed convergence direction for policy evolution, enabling the self-play process to stably approximate a Nash equilibrium.
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