Use with Regret Minimization

One of the algorithms implemented in Nashpy is called regret_minimization(), this is implemented as a method on the Game class:

>>> import nashpy as nash
>>> import numpy as np
>>> A = np.array([[3, -1], [-1, 3]])
>>> B = np.array([[-3, 1], [1, -3]])
>>> rps = nash.Game(A,B)

This regret_minimization method returns a generator of the outcomes of the regret minimization algorithm:

>>> ne_regret_mini = rps.regret_minimization()
>>> print(list(ne_regret_mini))
[([0.5, 0.5], [0.5, 0.5])]

regret_minimization takes the following parameters learning_rate and iterations.

>>> A = np.array([[3, -1,3], [-1, 3,6], [-1, 1,2]])
>>> B = np.array([[-3, 1,4], [1, -3,3], [-1, 3,4]])
>>> rps = nash.Game(A,B)
>>> learning_rate = 0.2
>>> iterations = 1000
>>> ne_regret_mini = rps.regret_minimization(learning_rate=learning_rate,
iterations=iterations)
>>> print(list(ne_regret_mini))
[([0.0, 1.0, 0.0], [0.0, 0.0, 1.0])]