This section presents an extension of the Direct Nash Optimization (DNO) framework to handle regularized preferences. The main difference between SPO and Nash-MD lies in the use of smoothed policies for the latter, which helps obtain a late convergence guarantee. The section introduces a new version of DNO, designed to converge to a Nash equilibrium using KL-regularization. The algorithm (Algorithm 3) works iteratively, adjusting the policy distribution through a partition function and a reward function, ultimately refining the policy with each iteration. This approach helps address the challenges of regularized preferences while ensuring stable convergence.
Extending Nash Direct Optimization for Regularized Preferences
Extension of the Direct Nash Optimization (DNO) framework to handle regularized preferences and ensure stable convergence to a Nash equilibrium using KL-regularization.
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