SlowGuess/NeurIPS_2023_OCR huggingface dataset:
E Additional Numerical Experiments
We illustrate the impact of  $\delta^{*}$  on the algorithm in the following example. We consider a 2-by-2 matrix game with the reward matrix
$$
r = \left[ \begin{array}{c c} 1 & 2 \\ 3 + \delta^ {*} & 3 \end{array} \right].
$$
We run the NPG algorithm with the same initial policy under various values of  $\delta^{*}$  ranging from  $1e^{-3}$  to 10. The NE-gap and the  $L_{1}$  accuracy over number of running steps are shown in Figures 2a-2b. As illustrated in the figure,  $\delta^{*}$  plays an important role in the convergence of the algorithm. Larger  $\delta^{*}$  results in faster convergence, which corroborates our theoretical findings.